Bibliographic record
Abstract
It is a great pleasure for us to contribute to this festschrift honouring Professor Jim Martin. His work has been inspiring in many ways but, in particular, we want to highlight two key areas – one more theoretical in the study of identification and participant tracking and one that is perhaps more personal: Jim’s work on reconciliation. Our study examines a text that offers some insight into reconciliation discourse. Reconciliation in Canada can, to some extent, be viewed as quite similar to reconciliation in Australia (Borsa, 2016) , for example, in setting up a kind of inquiry into the treatment of Aboriginal peoples. However, one aspect that differentiates it from all others is that ‘it did not have the kind of national and international attention that feeds into a broad public will to overcome a legacy of state sponsored harm’ (Niezen, 2017, p. 3)
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".