Bibliographic record
Abstract
LS - We should put it in context and say that it's part of a suite of projects that has been ongoing now for three years. We have been looking at the role of infrastructures and networks in the north, how there are some cultural resources that might be shared among the pioneers and first communities. Knowledge Clouds came out of an observation that Canada, as a circumpolar nation, is the only nation that doesn't have a university in its north. The reasons for that are multiple: partly political, but largely there isn't the population to sustain one or more permanent campuses in the north and so the project began as a question of 'Could one rethink the university in the context of geography, climate, and because of the meeting of many very different and unique cultures?'
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.159 | 0.045 |
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".