Exploring Cultural Layers: Code-Mixing in Bepsi Sidhwa’s “Ice-Candy-Man”

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Introduction

Code-mixing involves ‘languages’ blending or simultaneously using two languages in speech, often when a speaker or writer incorporates words from their native language due to a perceived lack of suitable expressions in the original language. Code-mixing occurs within sentences, it can lead to language hybridization, posing challenges to language maintenance and potentially contributing to language shift and eventual extinction.

The current study carries out a corpus-based code-mixing analysis in Bepsi Sidhwa’s novel “Ice-Candy-Man”. While Pakistani and Indian English literature has been extensively examined, there remains a lack of corpus-based research in this field. Daily interactions of bilingual individuals frequently exhibit instances of code-mixing [1; 2]. This phenomenon is also evident in South Asian novels written in Pakistani English featuring characters and speech groups from Pakistan. Following Pakistan’s independence, many writers produced post-colonial English literature as a response to colonialism, incorporating Urdu terms into their writings. Urdu, serving as a second language, holds significant esteem, and writers habitually integrate terms from their mother language into their writing when using the second language. Some researchers such as M.A. Malik, T. Azam, H. Pathan, and S. Khatoon [3] suggest that developing the teaching materials and assignments, teachers should consider the special needs of different mother tongue groups. This study emphasizes the importance of language in society and delves into code-mixing’s sociolinguistic aspects, examining how linguistic barriers, cultural variables, domains, intrinsic message aspects, physical contexts, and stylistic motivations contribute to code-mixed statements’ rationale. Code-mixing enriches readers’ understanding of authors and diverse cultures, playing a pivotal role in the evolution of various English dialects, a subject explored in this study, particularly within the post-colonial context.

The study is aimed at identifying and categorizing different types of code-mixing utilized Sidhwa’s “Ice-Candy-Man” and determining frequency of each category of code-mixing words representing cultural and social values.

J.F. Hamers and M.H.A. Blanc define code-mixing as the integration of components from one language into another, such as words, phrases, or clauses within the same sentence [4. P. 35]. The extent of code-mixing is subjective and perceived by the speakers of the language, sparking interest among readers and encouraging them to engage with the material. G. Ansre [5] highlighted early facts of code-mixing between English and West African languages in West Africa, emphasizing its role in demonstrating English’s impact on local languages. Code-switching and code-mixing have diverse definitions among different authors. V.L. Lanz [6], for instance, uses the term “code mixing” interchangeably with intra-sentential “code switching,” while others, like C.W. Pfaff [7], extend the definition of code-mixing to include borrowings. P. Muysken [8. P. 1] broadens the description to include variations in grammatical features. These varying perspectives contribute to a nuanced understanding of code-mixing in linguistic studies.

As for code-mixing in Eastern literary studies, it was investigated in terms of its deliberate use to preserve Eastern culture and challenge linguistic hegemony, see, for example, M. Taliya, A. Irfan, and X. Xing [9] whose research underscores the role of Ahmed Ali’s novel “Twilight in Delhi” in reconstituting the English language and advocating for linguistic diversity. Shanza Munir and Zahida Hussain [10] analyze the code switching and code-mixing in Nadeem Aslam’s novels and reveal the prevalence of intra-sentential switching and insertion, highlighting the representation of culture and mode of address in South Asian Englishes. The previous studies of Bapsi Sidhwa’s “Ice-Сandy-Man” focus mostly on discourse analysis and social problems, postcolonial themes and female characters [11–16]. The present work aims to explore how Bapsi Sidhwa contextualizes her message through the utilization of social and cultural expressions, employing the technique of code-mixing.

Theoretical approach of this research is based on the conceptual frameworks established by B. Kachru [17] and M. Modiano’s model of English [18], integrated with the Speech Act Theory [19] in order to elucidate the contextual meanings of words in specific circumstances and atmospheres. Code-mixing technique aims to convey genuine expressions occurring situations through communication and serves to portray local cultures and social values.

Research Methodology and Data Analysis

Corpus method was applied in this study as it helps to investigate the categories and frequency [20] of code-mixing words used in “Ice-Candy-Man”. Selected novel was downloaded from the internet and converted into word file for analysis. Sketch Engine corpus analysis tool was applied to analyze the data. Data was searched for term code-mixing, where the search term position was single word. Each code-mixing word was explored with its frequency to express local cultures and social values that was depicted by the author of novel.

In the present study world list was used to determine the total numbers and frequency of words, used in code-mixing to express the contextual in the certain circumstances. The code-mixed expressions collectively contribute to the religious and cultural diversity within the corpus. The numbers associated with each code-mixed expression indicate their frequency of occurrence in the corpus. It throws light on the prevalence and importance of these code-mixed terms.

1. The first group of words, that could not be avoided when an authentic picture of any cultural context is depicted, is a group of proper names. The central figure in any novel is usually a human, that is why the most numerous and frequent group of mixing words include lexemes naming people.

Table 1 combines anthroponyms, ethnonyms, kinship terms and naming words. The names appear to be predominantly in native languages, possibly reflecting names from a diverse cultural and regional background. The analysis aligns with the Speech Act Theory, as names contribute to performative acts, expressing cultural identity, affiliations, and interpersonal relationships. They play a vital role in shaping the communicative context and enriching the overall discourse

Possessive nouns presented in Table 2 involve code-mixing by combining English possessive forms with names of different linguistic origins (Arabic and Urdu). So, local native words are introduced into the English language grammatical system, mixing the languages. It proves use of mixing technique in the novel written in English instead of just mechanic borrowings of non-equivalent words. The mixing words found by the corpus manager are presented in Table 2.

The words from Tables 1 and 2 are complemented by a big number of addressing words that include proper names, honorific words, names of professions and positions, kinship terms, casts, religion and social status and so on. These addressing forms are original and do not repeat the words from the first table.

Table 1
Anthroponyms, ethnonyms, kinship terms, naming words

Sr. No	Word. No	Word	Frequency	Sr. No	Word. No	Word	Frequency
1	91	Imam	130	25	2402	Parveen	4
2	92	Adi	130	26	2442	Khatija	4
3	104	Din	113	27	2962	Daulatrams	3
4	124	Singh	93	28	3112	Ramzana	3
5	198	Rana	84	29	3209	Shankar	3
6	147	Masseur	77	30	3308	Baisakhi	3
7	151	Sikh	74	31	3816	Yakoob	3
8	194	Hamida	54	32	4292	Bhagwandas	2
9	222	Papoo	47	33	4593	Guptas	2
10	277	Khan	37	34	4880	Hindustan	2
11	290	Sharbat	36	35	4935	Ahmed	2
12	317	Muccho	33	36	5002	Brahmin	2
13	466	Punjabi	22	37	5451	Faiz	2
14	497	Nehru	21	38	5957	Ranjeet	1
15	551	Gandhi jee	18	39	6085	Bhagwan	1
16	763	Himat	13	40	6245	Ganga	1
17	667	Noni	11	41	6791	Sharmas	1
18	887	Moti	11	42	7315	Himat-ali	1
19	1053	Mumtaz	9	43	7858	Imtiaz	1
20	1109	Tara	9	44	8121	Deepa	1
21	1616	Tota	6	46	8530	Jawaharlal	1
22	1645	Shankars	6	47	8673	Jullunder	1
23	1795	Ravi	6	48	8680	Jumha	1
24	2195	Sethi	5

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Table 2
Possessive nouns

Sr. No	Word. No	Word	Frequency	Sr. No	Word. No	Word	Frequency
1	179	Ayah’s	62	16	4557	Shankar’s	2
2	502	Masseur’s	21	17	4766	Baijee’s	2
3	575	Singh’s	17	18	4956	Bankwalla’s	2
4	626	Adi’s	16	19	5948	Ramzana-the-butcher’s	1
5	649	Hari’s	15	20	6422	Gita’s	1
6	716	Papoo’s	14	21	6513	Sarkar’s	1
7	734	Rana’s	14	22	6768	Shahjehan’s	1
8	942	Chaudhry’s	10	23	6925	Sikh’s	1
9	1540	Hamida ‘s	6	24	6928	Sikhs’	1
10	1544	Shankars’	6	25	7866	Daulatram’s	1
11	2011	Muccho’s	6	26	7978	Thug’s	1
12	2434	Allah ‘s	5	28	8460	Aunty’s	1
13	2912	Sahib’s	4	29	8651	Judas’s	1
14	3415	Mullah’s	3	30	10591	Chidda’s	1
15	4198	Daulatrams’	3	31	11928	Raj’s	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Table 3
Addressing words

Sr. No	Word. No	Word	Frequency	Sr. No	Word. No	Word	Frequency
1	44	Ayah	364	40	5612	Vazir	2
2	209	Mini	49	41	5502	Jan	2
3	234	Aunti	45	42	5861	Punjabis	2
4	425	Pindo	24	43	5958	Ranjah	1
5	600	Sahib	16	44	6108	Bibi	1
6	651	Jana	15	45	6363	Ghalib	1
7	684	Chaudhry	14	46	6477	Sahibs	1
8	788	Roda bai	12	47	6769	Shah	1
9	938	Baigee	10	48	6915	Hakeem	1
10	1260	Chotay	8	49	6926	sikh-muslim	1
11	1424	Mussulmans	7	50	6934	Boa	1
12	1436	Pathan	7	51	7107	Hawaldar	1
13	1535	Baba	6	52	7195	Heer	1
14	1572	Bankwalla	6	53	7600	Sufijee	1
15	1618	Pakistani	6	54	7690	Dai	1
16	1632	Guru	6	55	8504	Janab	1
17	1973	Ayahs	5	56	8521	Jat	1
18	1983	Sohni	5	57	8552	Jamadar	1
19	2190	Sehra	5	58	8647	Judas	1
20	2312	Mussulman	4	59	8718	Kapadia	1
21	2532	Sarkar	4	60	8778	Khalsa	1
22	2904	Banya	3	61	8851	Sathi	1
23	3024	Janoo	3	62	9621	Maharaja	1
24	3157	Kashmiri	3	63	9626	Mai	1
25	3227	Gurus	3	64	9657	Malijee	1
26	3407	Chacha	3	65	9661	Mamajee	1
27	3540	Pahailwan	3	66	9775	Abba	1
28	4175	Swaraj	2	67	9810	Balmy	1
29	4236	Tongawallah	2	68	9814	Mehta	1
30	4404	Congress-wallahs	2	69	10085	Mota	1
31	4654	singh-jee	2	70	10161	Multani	1
32	4951	Amma	2	71	10180	Murdabad	1
33	4959	Mocha	2	72	10267	Chaprasi	1
34	5067	Musses	2	73	10806	Pahialwan	1
35	5068	Musslas	2	74	11071	Fakirs	1
36	5069	Muslin	2	75	11322	Vazirini	1
37	5073	Sufi	2	76	11763	Рukka-sahib	1
38	5283	Thug	2	77	11922	Zemindari	1
39	5360	Pandit	2	78	11927	Rajput	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Several words in the dataset exhibit code-mixing by combining English words with words from Hindi and Urdu, reflecting cultural and linguistic diversity. The frequencies of addressing words provide an indication of the usage prevalence of each term. The dataset, within the framework of Speech Act Theory, primarily involves assertive and expressive speech acts. The code-mixing serves as an expressive act, contributing to the cultural and linguistic diversity of the names.

The next block of words that are necessary for reflection of local cultures includes toponyms and more generally names of different places.

Table 4
Place names, toponyms

Sr. No	Word. No	Word	Frequency	Sr. No	Word. No	Word	Frequency
1	461	Jail	20	16	4545	Shahdara	2
2	474	Waris	22	17	4797	Balconies	2
3	545	Amritsar	18	18	54060	Pathankot	2
4	668	Mandi	17	19	5504	Peshawar	2
5	835	Kotha	12	20	6847	Guffaws	1
6	936	Mozang	10	21	6872	Gurdwara	1
7	1055	Shalmi	9	22	6892	Sialkot	1
8	1113	Bazaar	9	23	6894	Gymkhana	1
9	1201	Gurdaspur	8	24	6938	Simla-pahari	1
10	2123	Chawk	5	25	8252	Dehra	1
11	2158	Bhatti	5	26	8485	Jails	1
12	2551	Shalimar	4	27	8960	Kot-rahim	1
13	2650	Falettis	4	28	9646	Makipura	1
14	2734	Chungi	4	29	9964	Misri shah	1
15	3473	Darbar	3	30	10007	Mohalla	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

The place names appear to be predominantly in one language, possibly reflecting names from various geographical locations. The inclusion of names like Jail, Amritsar, Gurdwara, and others reflects cultural and historical references, providing insights into the cultural diversity and historical significance of the locations. In the context of Speech Act Theory the dataset predominantly involves assertive and locutionary acts, providing information about the existence and frequency of various place names.

2. The second big group of mixing words includes words describing realities. It covers words related to specific practices, daily routine, everyday items, cloths, food etc. One of the striking specific cultural features of the material is religious vocabulary.

Table 5
Religion terms

Sr. No	Word No	Word	Frequency	Sr. No	Word No	Word	Frequency
1	554	Allah	18	16	6458	Saalam	1
2	858	Mullah	11	17	6481	salaam-alekum	1
3	1424	Mussulmans	7	18	6482	Salaams	1
4	1945	Allah-o-Akbar	5	19	6770	Shaitans	1
5	2434	Allah’s	4	20	6771	Shaitan	1
6	2465	Koran	4	21	7600	Sufijee	1
7	2861	Badshahi masjid	3	22	7724	Illallah	1
8	3415	Mullah’s	3	23	8586	Dervish	1
9	3473	Darbar	3	24	8737	allah-ki	1
10	4413	Kasam	2	25	8802	allah-o	1
11	4232	Toba	2	26	9304	Azan	1
12	5068	Musslas	2	27	10395	Nikah	1
13	5069	Muslim	2	28	11908	Rahman	1
14	5004	Eid	2	29	11909	Rahim	1
15	5631	Kalma	2	30	8737	allah-ki kasam	1
16	5073	Sufi	2

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

In this context code-mixing enables speakers to convey nuanced meanings related to spirituality, greetings, and landmarks. The hidden meanings are deeply connected to religious sentiments and cultural references, allowing for a more nuanced and culturally rich communication. In the realm of Speech Act Theory, these code-mixed expressions serve various functions, such as expressing greetings, making promises, and discussing cultural or religious topics.

The next group of mixing words covers everyday realities, namely clothing names, names of different food, names of everyday items, words used in everyday life and traditional practices etc. Some semantic groups (clothing, food) are highlighted separately due to their greater number, the others are just mentioned in Table 8. In addition, verbs and phrases were found among mixing words, it illustrates mixing technique not only on lexical but also on grammatical level in one utterance. The following tables present the mixing words identified in the text under study.

Table 6
Clothing names

Sr. No	Word. No	Name	Frequency	Sr. No	Word. No	Name	Frequency
1	220	sari	48	20	9606	shalwars	2
2	570	lungi	17	21	4969	banyan	2
3	721	dohti	14	22	4972	banyans	2
4	885	shawl	11	23	6511	sari-blouses	1
5	1036	saris	9	24	6778	shalwar-kamize	1
6	1477	lungis	7	25	6802	shawls	1
7	1617	shalwar	6	26	7271	bosky	1
8	1941	burka	5	27	8738	achkan	1
9	2124	pyjamas	5	28	8769	burkas	1
10	2126	khaddar	5	29	8836	dhoti-clad	1
11	2262	dhoties	5	30	8968	kulah	1
12	2399	chuddar	4	31	8972	kurta	1
13	3171	sandals	3	32	8973	kulla	1
14	3332	bosky-silk	3	33	10152	muffler	1
15	3920	sari-clad	2	34	10162	multani-silk	1
16	4062	topi	2	35	10924	pashmina	1
17	4260	garara	2	36	11793	purdah	1
18	4451	sari-blouse	2	37	11820	pyjama	1
19	4555	shalwar-kamizes	2

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

The dataset reflects a diversity of code-mixed clothing items, with varying frequencies, indicating the prevalence of specific items in the given context. The dataset, within the framework of Speech Act Theory, primarily involves assertive and expressive speech acts. The code-mixing serves as an expressive act, contributing to the cultural and linguistic diversity of the descriptions. The various clothing items and their frequencies provide a comprehensive overview of the garments associated with South Asian cultures.

This table also exhibits examples of code-mixing, where English words are combined with words from South Asian languages, particularly Hindi and Urdu. The words naswar, tobacco-naswar, Gurkha, Paan indicate the drug nature people living in the society. The dataset primarily involves assertive and expressive acts. The code-mixing serves as an expressive act, contributing to the cultural and linguistic diversity of the food descriptions. The various food items and their frequencies provide a comprehensive overview of the culinary items associated with South Asian cultures.

The words cover a range of contexts, from cultural and traditional terms to everyday words, objects, expressions, some of which are daily usable or related to specific cultural contexts, providing a diverse set of terms with varying frequencies.

The dataset showcases makes a mix of English and Hindi and Urdu words and involves locutionary acts, where linguistic expressions has been used to convey information about various items. The code-mixing further enhances the linguistic dimension, serves as an expressive act, contributing to the cultural and linguistic diversity of the object descriptions. It is important to say that some of Hindi and Urdu words from this table have equivalents in English (Murg — chicken, Ghoongat — snail, Aatish — fire, Glee — throat, Gulab — rose etc.). In addition to nouns, adverbs, pronouns, conjunctions, and prepositions are presented (Tu — so, Aab — now, Pry — beyond, Meri — mine, Mujh, Mujhe — me, etc.), this illustrates insertion of material from one language into the structure from the other language [8. P. 3]. No less interesting is the use of verbs and phrases in the text under study, see examples in Tables 7 and 8.

Table 7
Food names, gastronymes

Sr. No	Word. No	Name	Frequency	Sr. No	Word. No	Name	Frequency
1	1183	Chapatties	8	12	6362	Ghadka	1
2	1254	Hookah	8	13	6857	Gulab-jamans	1
3	2097	Chapatti	5	14	7703	Dal	1
4	2154	Paan	5	15	8490	Jalebis	1
5	3115	Raspberry	3	16	8748	Kebab	1
6	3228	Gurkha	3	17	8775	Rutti	1
7	3872	Makhan	2	18	8961	Korma	1
8	3887	Biryani	2	19	9651	Malida	1
9	4634	Halva	2	20	9734	Masala	1
10	5345	Pakoras	2	21	10272	Naswar	1
11	5378	Parathas	2	22	10738	Tobacco-naswar	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Table 8
Routine words and object names

Sr. No	Word. No	Name	Frequency	Sr. No	Word. No	Name	Frequency
1	723	Tonga	14	38	4978	Eddy	2
2	1048	Charpoy	9	39	4993	Mortar	2
3	1278	Khaki	8	40	5484	Jab when	2
4	1614	Tongas	6	41	5513	Jashan	2
4	1871	Mugs	5	42	5933	Zamana	2
5	1822	Ghoongat	5	43	6430	Blabs	1
6	2470	Koochuck	4	44	6592	Gota	1
7	2541	Kohl	4	45	6853	Gulab	1
8	2838	Zindabad	4	46	6366	Ghar	1
9	2324	Gullies	4	47	6779	Sham	1
10	2704	Attar	4	48	7369	ho-o-o-li	1
11	2819	Punka	4	49	7114	Hawkish	1
12	2324	Gullies	4	50	7108	Hawa	1
13	2991	Charpoys	3	51	7369	table-fan	1
14	3172	Dhurrie	3	52	7804	Tamba	1
15	3256	Siri	3	53	7827	Darwaza	1
16	3393	Baraat	3	54	7404	Holi	1
17	3644	Billa	3	55	7605	Brats	1
18	3768	Pry	3	56	8254	Tu	1
19	3101	Bengal	3	57	8298	Tusi	1
20	3307	Dough	3	58	8862	Aab	1
21	3317	Maroon	3	59	8954	Koi	1
22	4419	Glee	2	60	8459	Bulb	1
23	4658	Sissy	2	61	8583	Jharoo	1
24	4669	Bodhi	2	62	8785	khut-putli	1
25	4677	Loot	2	63	8822	Dhawan	1
26	4746	Doolha	2	64	8858	Dhurries	1
27	3875	Gaudy	2	65	8959	kookadaru	1
28	3994	Gutter	2	66	8969	Kuch	1
29	4109	Cuddly	2	67	9117	Aatish	1
30	4147	hulla-goolla	2	68	9250	Cajoles	1
31	4154	Brat	2	69	9855	Meri mine	1
32	4336	Bihar	2	70	10184	Murg	1
33	4535	Settee	2	71	10396	tabla-drum	1
34	4539	Shabash	2	72	10773	Ankhe	1
35	4480	Whacks	2	73	10153	Mujh	1
36	4605	Baar	2	74	10157	Mujhe	1
37	4611	Guzri	2	75	10820	Paisay	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Table 9
Verbs

Sr. No	Word. No	Name	Frequency	Sr. No	Word. No	Name	Frequency
1	4587	Aye	2	10	7068	Hasi	1
3	4642	Bachao	2	11	7786	Talash	1
4	4762	Bolay	2	12	8282	Dekhna	1
5	5491	Jai	2	13	8287	Dekko	1
6	5548	Israr	2	14	8735	Karmas	1
7	6127	Bhool	1	15	9546	Badmashi	1
8	6431	Bizarre	1	16	9751	Masti	1
9	6971	Siski	1	17	8281	Dekho	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

This table represents how Urdu and Hindi verbs are used to create colloquial tone and strong expression in the communication. These verbs cover a range of actions, emotions, and expressions (for example, aye — (informally) come, callow — behave, bachao — save, help, bolay — says, jai — vin (victory, glory), israr is used for insist, reflects determination or emphasis. Verbs with frequency 1 — bhool, hasi, talash, dekhna, dekko, karmas, badmashi, masti, dekho have been used to express the strong contextual meanings according to situation (dekko and dekhna involve seeing or looking; hasi and masti are related to laughter and enjoyment. Badmashi implies mischief, etc.). The chosen verbs carry emotional nuances, contributing to the tone of the communication.

Table 10
Phrases

Sr. No	Word. No	Word	Frequency
1	7038	hari-alias-himat-ali’s	1
2	7039	hari-alias-himat-ali	1
3	7237	henna-decorated	1
4	7405	holi-with-their-blood	1
5	7792	talcum-powdered	1
6	9130	shab-e-intezar	1
7	10573	chi-chi-chiwallal	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

The given phrases contribute to express the emotions, identity and reactions, the meanings of these phrases rely on shared cultural knowledge and context. Phrases involving names or identities contribute to personal acts, shaping individual or group representations. The material showcases a diverse range of code-mixed expressions, incorporating cultural, emotional, and identity-related elements. The use of multiple languages and cultural references adds depth and nuance to the communication, making it rich and context dependent.

3. The third group of mixing words is associated with the emotional and expressive stylistic function of language. It includes number of interjections and exclamations as well as invectives.

Table 11
Expressions, interjections and exclamations

Sr. No	Word. No	Word	Frequency	Sr. No	Word. No	Word	Frequency
1	260	Oh	39	23	4163	Hush	2
2	356	Oye	29	24	4195	Teetering	2
3	853	Chi	11	25	4574	Shies	2
4	1277	Ho	8	26	4794	Soo	2
5	1289	Wah	8	27	5266	Oof	2
6	1423	Arrey	7	28	5758	Wha	2
7	1538	Ha	6	29	7320	ah-ha	1
8	1838	Ah	5	30	7375	aha-hurrr	1
9	1888	Na	5	31	7521	aiiii-yo	1
10	2003	Eeriee	5	32	7649	hush-hush	1
11	2177	Um	5	33	8607	a-y-a-h	1
12	2308	Hum	4	34	8996	Whooooo	1
13	2350	Tch	4	35	9118	Aaaaaa	1
14	2362	Tendon	4	36	9213	Yaaaa	1
15	2828	Ya	4	37	9219	Yay	1
16	2840	Shoo	3	38	10476	tch-tch-tch	1
17	2995	Hisses	3	39	10585	oh’s	1
18	3026	Jee	3	40	10590	oh-ho	1
19	3334	Hey	3	41	11056	Ummm	1
20	3599	Umm	3	42	11415	pooch-pooch	1
21	3856	slurp-slurp	2	43	11936	Aeeee	1
22	3865	Hay	2

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Table 9 consists of interjections and exclamations that carry cultural and emotional connotations. These interjections and their potential hidden meanings, analyzed in the perspective of Speech Act Theory, have different origins. Some of them are quite universal (Oh, Ah, Ha, Hey, etc.) the others are rather culturally specific (Oye — attention-seeking, calling someone, or expressing surprise; Chi — disgust and disapproval, Wah — praise or admiration, Arrey attention-seeking, surprise, or disbelief, Eeriee — surprise, realization, and emphasis, etc.). These interjections play a crucial role in communication by not only expressing emotions but also shaping the overall tone and style of the interaction, they go beyond literal meanings, influencing the social dynamics and emotional atmosphere in a conversation.

Another emotional and highly expressive linguistic means is an invective that is often culturally specific.

Table 12
Invectives

Sr. No	Word. No	Name	Frequency	Sr. No	Word. No	Name	Frequency
1	1184	Goondas	8	13	7011	Haram-khor	1
2	1547	Badmash	6	14	7020	Haramzadas	1
3	2273	Dungarwadi	4	15	7021	Haramzada	1
4	3352	Duffa	3	16	7023	Haramzadi	1
5	3482	Tamasha	3	17	8327	Buckwas	1
6	3624	Jinn	3	18	8366	Ullu-kay-pathay	1
7	4405	Giders	2	19	8367	Uloo	1
8	5330	Choorail	2	20	8698	Jungly	1
9	5410	Buck	2	21	10396	Nimak-haram	1
11	6570	Goonda	1	22	10809	Choorails	1
12	6571	Goondaish	1

 Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Table 10 presents the invectives, swearing, abuse words that are used to describe unruly behavior and colloquial tone of the communication. The words are derived from Arabic, Urdu and its dialects and are used as colloquial expressions to describe aggressive behavior, for example, goondas (Urdu) refers to ruffians or hooligans, badmash is used to rogue or mischievous action, dungarwadi shows regional and cultural specificity to the description of people, jinn (Arabic) refers to supernatural beings or spirits and invokes a sense of mystery and the supernatuality in people, choorail is often used colloquially to refer to a witch or a frightening woman and carries a derogatory connotation, haram-khor describes someone engaging in forbidden or unethical activities, it carries a strong, judgmental tone, often used to express disapproval, etc. It is interesting that some of compound words also demonstrate mixing technique, since one morpheme in their structure is originally Arabic the other is from Urdu (for example, haram-khor, haramzada, nimak-haram).

So, three groups of mixing words with different semantics, grammatical features and pragmatic functions were identified in the research material. The corpus-assisted analysis of the text allowed find some interesting results related to code-mixing and developing Indian and Pakistani English.

Findings and Discussion

The analysis of code-mixing in Bapsi Sidhwa’s novel “Ice-Candy-Man” provides insightful findings, revealing a deliberate and effective use of linguistic strategies to convey cultural, religious, and social nuances. Employing a qualitative method and drawing on conceptual frameworks such as Kachru (1983), Modiano’s model of English (1999), and Speech Act Theory, the study explored various categories of code-mixed words and their frequencies.

The total number of selected non-English mixing words is 461, that is 3,9% of all words in the book analyzed, among them the number of unique mixing words is 391, some of them get grammatical forms in English (possessive, plural forms, compound words). The number of mixing words with the frequency 1–2 is 180, that is 46% of total numbers of words analyzed. This fact indicates a wide variety of mixed vocabulary included in the text. The study identified distinct categories of code-mixing, with varying frequencies. The diversity of code-mixed words reflects the nuanced approach employed by the author to convey different cultural and linguistic elements. The frequencies of each category provide insights into the prominence of certain themes within the narrative. The given chart expresses the use of code-mixing in English language:

Fig. 1. Diversity and frequency of mixing words
Source: Habibullah Pathan, Urooj F. Alvi, Oksana I. Aleksandrova, Nighat Sultana’s study.

Categorization of words derived by Sketch Engine for corpus analysis should be carried out on the basis of several factors: 1) the presence of equivalence in the second language, 2) belonging to a semantic group, 3) sphere of use, 4) grammatical features, 4) pragmatic and stylistic function.

The analysis of names has revealed a mix of linguistic origins, suggesting a diverse cultural landscape. Possessive nouns were code-mixed to emphasize relationships and cultural identities, these forms illustrate grammatical insertion of native words into English. The analysis of addressing names highlights instances of code-mixing in personal identities. The frequencies of specific names add layers of cultural richness to the narrative, showcasing a deliberate selection of terms. Code-mixing contributes to a diverse linguistic landscape, reflecting the multicultural context. The analysis of place names indicated a predominantly single linguistic or cultural origin, with an absence of clear instances of code-mixing in this category. While historical and cultural references are present, the dataset suggests a more focused approach to maintaining the authenticity of specific locations.

The incorporation of religious terms emphasized the novel’s focus on cultural and religious diversity. These instances contributed to a rich portrayal of spirituality and traditions, aligning with the broader cultural context. The dataset reveals a diversity of code-mixed clothing items, with varying frequencies. Terms like Sari, Lungi, and Dohti predictably exhibit high frequencies, emphasizing their cultural significance. Code-mixing is evident in food-related terms, combining English with Hindi and Urdu. High-frequency items like Chapatties and Hookah reflect their cultural prominence. The dataset provides a comprehensive overview of code-mixed culinary expressions, adding cultural and linguistic richness. Code-mixing in daily usable words contributed to a vibrant and expressive narrative. These terms perform assertive and expressive acts, conveying cultural and emotional nuances in various contexts.

Verbs in the dataset contribute to a colloquial tone, expressing actions, emotions, and emphasis. The chosen verbs carry nuanced meanings, contributing to the overall tone of the communication. Phrases involve names, emotions, and cultural references contribute to personal acts and identity representation. The meanings of phrases are context-dependent, relying on shared cultural knowledge. Use of native phrases “lead to more complete activation of the second grammar” [8. P. 9], making code-mixing more evident. Emotional dynamics is shown by code-mixing with authentic interjections, colloquial words and invectives, adding cultural specificity and emotional intensity to the descriptions.

The analysis indicated effective use of code-mixing in various linguistic contexts. The dataset reflected a conscious effort to convey cultural, emotional, and identity-related nuances, enriching the overall communication. Code-mixing emerged as a powerful linguistic tool, shaping the narrative and fostering a deeper understanding of the cultural and linguistic diversity within the given contexts.

Conclusions

The analysis of code-mixing in Bapsi Sidhwa’s novel “Ice-Candy-Man” provided a comprehensive understanding of its deliberate and effective use to convey cultural, religious, and social nuances. The study, employing conceptual frameworks such as Kachru’s, Modiano’s model of English, and Speech Act Theory, was explored various categories of code-mixing and their frequencies. Findings revealed a diverse linguistic landscape within the novel, emphasizing the importance of code-mixed expressions in portraying cultural richness. The incorporation of diverse names, possessive nouns, addressing forms, religious terms, clothing and food items highlighted the novel’s emphasis on cultural, social and religious diversity. Routine words, verbs, phrases, conjunctions, expressions, and invectives contributed to a vibrant and expressive narrative, reflecting a conscious effort to convey nuanced meanings. Speech Act Theory underscores the multifunctional role of code-mixing, serving various communicative purposes beyond its linguistic function. Additionally, the study was suggested potential impacts on language maintenance and shift, as code-mixing challenges language purity while emphasizing the value of native languages. It provided a robust foundation for exploring the intricate interplay between language, culture, and communication. Consequently, code-mixing in Bapsi Sidhwa’s novel is a deliberate and effective linguistic strategy, contributing to a nuanced and culturally resonant narrative. It sheds light on the complex dynamics within post-colonial literature, prompting further exploration of language, culture, and effective communication.

About the authors

Habibullah Pathan

Sohar University

Email: Hpathan@su.edu.om
ORCID iD: 0000-0003-3425-3594
Scopus Author ID: 57221613434
ResearcherId: AAV-7602-2020

PhD in Philology, Research Professor, Faculty of Language Studies

Al Jameah Street, Sohar, Al-Batinah, Oman, 3111

Urooj F. Alvi

University of Education

Email: Urooj.alvi@ue.edu.pk
ORCID iD: 0000-0002-8545-0136

PhD in Philology, Assistant Professor, Department of English, Division of Arts and Social Sciences

Rati Gun Rd, Lower Mall, Data Gunj Buksh Town, Lahore, Punjab, Pakistan, 54000

Oksana I. Aleksandrova

RUDN University

Author for correspondence.
Email: alexandrova-oi@rudn.ru
ORCID iD: 0000-0002-7246-4109
Scopus Author ID: 57200073938
ResearcherId: Q-7339-2016

PhD in Philology, Associate Professor, Associate Professor of the General and Russian Linguistics Department, Faculty of Philology

6, MiklukhoMaklaya str., Moscow, Russian Federation, 117198

Nighat Sultana

Lahore Leads University

Email: nighatsultana41@gmail.com
ORCID iD: 0009-0009-2876-7282

Lecturer, Department of English

5, University Phase, Kamahan, Lidher Rd, Lahore, Punjab, Pakistan, 54000

References

Supplementary files