Two-eyed critical sensemaking : an integrative approach for studying post-TRC organizational change
Bibliographic record
Abstract
The research method, Two-Eyed Critical Sensemaking, is a novel integrative approach for \nstudying the relationship between policy documents and enactments of respectful reconciliation focused organizational change. Drawing from the guiding principle of Etuaptmumk/Two Eyed Seeing, I define six iterative methodological steps that weave together two qualitative critical research approaches - Culturally Relevant Gender-Based Analysis (CRGBA), an Indigenous-led view of applied policy studies, and Critical Sensemaking, a Canadian-led view of management and organization studies. I use the Truth and Reconciliation Commission (TRC) of Canada as a case study to demonstrate the function of the Two-Eyed Critical Sensemaking method. The TRC sparked public concern regarding Canada-Indigenous reconciliation and a discursive shift that \ntransformed reconciliation into a defining political issue in Canada. But, the meaning of \nreconciliation is inconsistently used in practice. I propose the TRC presents a model for \nreconciliation-oriented organizational change for Canadian administrators responding to the Commission’s Calls to Action.
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 | 0.103 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.022 | 0.093 |
| Scholarly communication | 0.031 | 0.023 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".