Bibliographic record
Abstract
To achieve greater societal impact within a highly competitive charitable marketplace, the four major, national, injury prevention organisations have decided to collaborate toward the following purposes: Long-term goal To save more lives and prevent more potentially life-altering injuries in Canada. Medium-term objective To raise awareness and to facilitate attitudinal and behavioural shifts among the organisations respective target populations, in partnership with their natural allies, of best practices in preventing injuries, and injury-related deaths in Canada. Short-term output The development of recommendations, an implementation plan and supporting documentation for a game changer that will build their capacity as individual organisations to promote more effective and efficient strategies and programming. Seek better ways and means to identify, integrate and approve the collaborative delivery of: ▶ knowledge creation, synthesis and transfer ▶ stakeholder engagement ▶ fund development ▶ marketing. This presentation will share the outcomes of phase I of the study in terms of defining the ideal inputs and outputs, catalogue these inputs and outputs in relation to the sponsoring organisations, describe what is unique about those inputs and outputs among the sponsoring organisations and what is shared by two or more of them, project and outline the benefits that the collaborative delivery of the function could bring, advance recommendations for amending existing processes, or introduce new ones for more suitably identifying, overseeing, managing and assessing future shared initiatives within each function, describe one or more approaches for resourcing such collaborative processes on a go-forward basis, propose a strategic or 3-year outlook for the frameworks operation.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.008 |
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".