Bibliographic record
Abstract
Speakers have a particular interest in reinforcing proper ethical behaviour. All presiding chairs are cognizant that a respectful and courteous demeanor on the part of members can take the poison out of the atmosphere, can calm a stormy house, and facilitate the restoring of parliament to its ideal state where the fiercest controversies can take place within an ambit of mutual respect, personal honour, and regular procedure for the protection of all opinions, even those of the smallest minority. Other professions, including Judges, have attempted to apply ethical theory to real-life situations and to establish standards for ethical conduct. Since 2000, the Canadian Centre for Ethics and Corporate Policy together with the Conference Board of Canada has been holding a Business Summit to talk about the challenges and potential for business ethics in Canada. Many federal government departments have established the position of Officer of Public Service Values and Ethics for their employees, while smaller departments and agencies assigned additional responsibility for values and ethics to existing executives. This article suggests that Speakers could adopt a Declaration of Ethics for Presiding Officers to outline the importance of ethics in parliamentary institutions.
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 | 0.061 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.033 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 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".