Whether the Freedom of Information and Protection of Privacy Act supports the decision of Service Nova Scotia and Municipal Relations to disclose a Pre- Employment Reference Check.
Bibliographic record
Abstract
portion of a Pre-Employment Reference Check given by the Third Party. SNSMR received an application for access to a record under the Freedom of Information and Protection of Privacy Act (“FOIPOP”). The Applicant sought access to opinions and views contained in reference checks provided by Third Parties concerning the Applicant. In accordance with Section 22(1) of FOIPOP, SNSMR notified the Third Party of the application. The Third Party did not consent to the disclosure of the Pre-Employment Reference Check. Initially, SNSMR decided to withhold the entire record. However, in a letter dated July 4, 2006, SNSMR notified the Third Party of its decision to release the document in part. SNSMR decided to sever the third party’s personal information from the record and disclose to the Applicant any views or opinions made about the Applicant by the Third Party. SNSMR’s rationale for the decision was based on French v. Dalhousie University, 2003, NSCA 16 wherein the Court concluded that a person’s views or opinions about someone else are not the author’s personal information but rather the personal information of the subject.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".