Hood initiation: Jewels of leadership or jinns of temptation
Bibliographic record
Abstract
(A Poetic Introduction) I envision myself standing on the bank of the tenure stream. Can I make it to the other side without perishing along the way? I wonder if my publication record is yet strong enough to ensure me safe passage to the other side of the stream. Perhaps I will perish in the waters of initiation before I can even begin an Margie Buttignol is an academic career... independent scholar and ^ ^ ,g ^ J wonder Touching ^ teac erwit te oronto bottom ofthe stream with my toes I feel a steep incline Catholic District School begin. I cling to the familiar bank of my doctoral Board. C. T. Patrick student identity even after I have defended my thesis Diamond is a professor and been ritually hooded at convocation. Positioned with the Centre for at the bank of the tenure-stream, I imagine my self Teacher Development of surrounded by the waters of initiation into Academe. the Ontario Institute for But> as 1 lineer therememories of another initiation Studies in Education of flood mt0 mind ~ my entry11110 the mysterious the University ofTornto W0IM of Brownies when I was seven years old... Toronto, Ontario, According to Campbell (1949), we re-live the same Canada. experience over and over again in spirals. Is this why
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".