Bibliographic record
Abstract
Are current service performance measures and reporting mechanisms .......2-37 appropriate and effective?b.Are appropriate mechanisms in place to ensure service standards and key .2-39performance indicators are tracked and met?Are appropriate benchmarks established and tracked?c.If not, what changes should be made to ensure the ongoing accountability .2-40 of the WCB for fair, responsive, and timely delivery of service to workers, employers and the public?Section 3 reports our recommendations.These are derived from the activities conducted under the Service Delivery Core Review, as well as our decade-long interaction with the WCB.They reflect only the issues within the mandate of Part 1 of the Core Review, "Service Delivery."Given that mandate, they represent the issues that we feel are most critical to resolving the service problems at the WCB.They generally do not touch upon the subjects that were detailed for Part 2 of the Core Review, "Governance, Appellate Structure, Major Law, Policy and Regulation Review," although we are generally aware of the contents of that part of the Core Review as well.
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 | 0.064 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.027 |
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".