Environment \n& \nNatural \nResources
Bibliographic record
Abstract
The importance of the environment \nand natural resource development \nfor the province of \nNewfoundland and Labrador is hard \nto overstate, and the works that the \nHarris Centre has supported over the \npast ten years reflect this far reaching \nimporantance. And while the works \nCentre supports on the environment \nand natural resources has predominantly \nfocused on Newfoundland \nand Labradorian, the high-level purpose, \nscope and impact of research \ndone in Newfoundland and Labrador, \nis not limited to the province. \nFor example, if there is a \nstudy of the impact of wind generated \nenergy in Newfoundland and \nLabrador, the findings likely will have \nbeen focused on provincial policy development, \nbut they could also have \nan impact nationally and internationally. \nOn top of this, the environment \nand natural resources have a \nbroad range of social, economic and \nenvironmental impacts that mean \nthese sectors have far reaching implications \nfor many facets of society. \nThis first part of the report \nprovides an overview of what is \nmeant by the environment and natural \nresources, as well as engaged scholarship \nand how these fit with the role \nof the Harris Center in supporting \nresearch and framing policy debates \naround these issues in the province. \nThe second part of this report focuses \non lessons learned through research \nand events related to particular \nsectors that were supported by the \nHarris Centre. The report concludes \nwith recommendations for how the \nHarris Centre might be better able to \nrespond to stakeholder concerns in \nthe works they support on the environment \nand natural resources
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.346 | 0.132 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".