In Search of Common Ground: Reconciling Western-based Governance Principles and First Nations Traditions” Institute on Governance, http:///www.iog.ca
Bibliographic record
Abstract
The Institute On Governance (IOG) is a Canadian, non-profit think tank that provides an independent source of knowledge, research and advice on governance issues, both in Canada and internationally. Governance is concerned with how decisions important to a society or an organization are taken. It helps define who should have power and why, who should have voice in decision-making, and how account should be rendered. Using core principles of sound governance- direction and purpose; legitimacy and voice; accountability and transparency; effective performance; and ethical behaviour and fairness – the IOG explores what good governance means in different contexts. We analyze questions of public policy and organizational leadership, and publish articles and papers related to the principles and practices of governance. We form partnerships and knowledge networks to explore high priority issues. Linking the conceptual and theoretical principles of governance to the world of everyday practice, we provide advice to governments, communities, business and public organizations on how to assess the quality of their governance, and how to develop programs for improvement. You will find additional information on our activities on the IOG website at
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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".