Scaling the Ivory Tower: Engaging Emergent Identity as Researcher
Bibliographic record
Abstract
This article explores a graduate student’s process of embracing emergent identity as a researcher in the field of counselling, and how this is facilitated through mentoring relationships. I apply concepts from Winnicott’s object relations theory (1958, 1965, 1971) and Lave and Wenger’s (1991) sociocultural model of situated learning to my own experiences of doctoral studies at the University of British Columbia. Through the use of personal narrative informed by these two theoretical perspectives, the article explores and reflects upon how the relationship between research supervisor and graduate student can contribute significantly to the student’s personal and professional development and emerging identity as a researcher and scholar.
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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 | 0.040 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".