How Many Participants Can I Have? A Collaborative Autoethnography Examining Graduate Student Access to Teacher Participants
Bibliographic record
Abstract
This collective autoethnography intends to:(1) bring attention to current power structures that prevent interaction between graduate students and teacher participants; and, (2) highlight the difficulty in completing graduate research caused by these hegemonic structures. We use Foucault’s (1997) ideas on power and state of domination to examine power structures and norms within school systems in Canada. Autoethnography encourages individuals to engage their experiences in relation to social discourses and analyses. Through collaboration, we were able to combine our experiences to find intersections and power relations between graduate students and the systems they attempt to study. We contend that i n blocking interactions between graduate students and teachers, school systems are able to maintain their state of domination over teachers, discouraging an ethic of truth-telling. T his opposes the ideals of a democratic education, which does not dictate a purpose of education but constantly discusses and deliberates what it means to be a “good” teacher or to have a “good” education (Biesta, 2007).
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".