An interview with Avigail Eisenberg: “Reasoning about the Identity of Aboriginal People”
Bibliographic record
Abstract
Avigail Eisenberg is an Associate Professor in the Department of Political Science, University of Victoria. She was also a fellow of CRÉUM during the 2004-2005 academic year. She has written important work on the issues of identity, race, gender, minority rights, and in particular, Aboriginal claims. Her writing displays intelligent and acute commentaries in which she demonstrates an ability to tackle difficult questions in a refreshing way. A list of her publications is available at http://web.uvic.ca/polisci/eisenberg/pubs.htm . Martin Blanchard of CRÉUM asked Professor Eisenberg if she would be willing to be interviewed via email on the subject of an article she had just finished writing, entitled “Reasoning about the Identity of Aboriginal People”. (This text is forthcoming in Accommodating Cultural Diversity: Contemporary Issues in Theory and Practice, Stephen Tierney ed., London: Ashgate.) She kindly accepted to answer his questions.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.014 |
| 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".