Bibliographic record
Abstract
This paper documents the identity negotiation and strategic language efforts of a female postgraduate learner of English from a rural Arab background. She needed to obtain a high score in the Test of English as a Foreign Language (TOEFL) exam as a prerequisite to getting an unconditional offer from a UK university. The narrative inquiry reported here used a series of individual semi-structured interviews as the main data, with a variety of means, including written narrative and email exchanges. It is guided by Dörnyei [(2009). The L2 motivational self system. In Motivation, Language Identity and the L2 Self, ed. Z. Dörnyei and E. Ushioda, 9–42. Bristol: Multilingual Matters] concept of ‘possible selves’, and Norton [(2000). Identity and Language Learning: Gender, Ethnicity and Educational Change. Harlow: Longman/Pearson Education] notion of investment in language learning and the distinction between compulsory (i.e. largely regulated by cultural beliefs) and voluntary (i.e. basically internalised within the self) strategies. The data suggest that Noura's language strategic efforts were significantly constrained by her social networks, which limited her personal investment in language learning. Nonetheless, after receiving a Master's grant, Noura acted agentively to accomplish her ultimate vision of being the first women in her village to complete her postgraduate studies abroad, and participate in benefiting her country (i.e. individual and national interest). She did so through building a strong relationship with a Canadian colleague in her workplace, together with adopting other strategies. From this case study, pedagogical implications as well as areas for ongoing research are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".