Research Confidentiality: Researcher and Institutional Responsibilities:
Bibliographic record
Abstract
On October 16, 2015, Dr. Ted Palys visited Langara College to present a fascinating talk on recent developments in Canadian case law and ethical issues regarding the protection of confidentiality when it comes to research information provided by research participants. In his presentation, Dr. Palys lays out the guidelines of the Tri-Council policy and provides a better understanding of the law around the protection of research confidentiality and the professional responsibilities of researchers and institutions. \nDr. Palys is a professor in the School of Criminology and an associate member of the Department of First Nations Studies at Simon Fraser University (SFU). His most recent publication is a 2014 book co-authored with John Lowman called, "Protecting Research Confidentiality: What Happens When Law and Ethics Collide." \nThe event was organized by Dr. John Russell, Chair of the Langara Research Ethics Board, in partnership with Langara's Scholarly Activity Steering Committee.
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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 | 0.248 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.030 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".