Writing the dissertation proposal: a comparative case study of four nonnative- and two native-English-speaking doctoral students of education
Bibliographic record
Abstract
The textual features of the proposals as well as the processes of producing them were influenced by the social/disciplinary contexts in which the students were situated and the personal states of the students. The processes of writing the proposal varied more among each individual student rather than between the NNS and NS speakers even though some NNS students reported that they spent more time on editing their proposals and they faced big challenges in the English language and academic writing conventions. I found, in analyzing the proposals, that differences in the students' written texts were less related to their linguistic or cultural backgrounds but more to the ideology and epistemological and methodological norms and conventions of their disciplines or programs of study. Through the lenses of 6 doctoral students of education, their dissertation proposals, and their mentors, this study describes the context, processes, and products of the students' proposal writing. It also analyzes fundamental influences on graduate students' proposal writing and areas of individual differences (besides linguistic backgrounds) among the students' writing which affected the textual features of the students' proposals and their processes of producing the proposals. Data were collected over a period of 10 months in 2 graduate programs in education in a large Canadian university from 4 nonnative-English-speaking (NNS) and 2 native-English-speaking (NS) doctoral students of education and from 5 professors who were nominated by the students and deemed most familiar with these students' thesis proposal writing. The data consist of interviews, a questionnaire completed by each student, 6 dissertation proposals, and other written documents produced by the students and their professors. The study has several implications for theory, pedagogy, and future research. The construct of NNS versus NS was problematic in practice. Both NNS and NS graduate students need ongoing support from their professors and writing instructors or services while they engage in disciplinary writing. Some NNS students may need more help with grammar, vocabulary, and styles of academic genres than their NS counterparts do. Immersion/participation in disciplinary discourse communities and explicit instructions on discipline-specific writing norms/conventions and the English language itself are of equal importance to success of the students' academic enculturation.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".