Bibliographic record
Abstract
I moved there from central Canada when I was 25 years old. I’m one of the “anti-statistics ” if you will, because all the time in the news we hear about the trend of young people moving away from this province, and as someone who went the other way I can tell you that my experience is somewhat unique. So I thought I’d offer you some points of view from that perspective, as a young person who moved TO and not away from Nova Scotia. First of all, I didn’t move here for a job, I moved here for quality of life. So if this proposed project flaunts itself as the answer to people moving away, there had better be a unit, as though one job is the same as any other, as though people move here or stay here just for the almighty job. You could look at this proposal like any new neighbour moving into a place, except this one happens to be a corporate neighbour who will have more impact on the place than your average citizen. And you might say, well, we don’t mind having new neighbours, as long as they don’t harm what’s already here, which may be a small community but it is a
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.389 | 0.218 |
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".