Indigenous Education Leads' Stories of Policy Enactment: A Sociomaterial Inquiry.
Bibliographic record
Abstract
Investing in Indigenous education has been identified as a key priority by provinces and territories acrossCanada. In response, the Ontario Ministry of Education (OME) introduced the Ontario First Nation,Metis, and Inuit Education Policy Framework (the Framework). This policy directive outlines the OME’scommitment to working in partnership with Indigenous and non-Indigenous educational stakeholders toincrease the capacity of the public education system to respond to the learning and cultural needs of theestimated 50,312 Indigenous students who attend Ontario’s 5,000 elementary and secondary schools.While substantial progress has been made since the Framework’s release, more work is needed to ensureall students gain an understanding of, and appreciation for, Indigenous cultures, experiences, and perspectives. One way the OME has shown their continued investment is through the sustained allocation of fundsfor Indigenous Education Leads (Leads). Since the Framework’s release in 2007, these individuals haveplayed an invaluable role in supporting the implementation of the Framework. However, little is knownabout their lived experiences. Thus, the purpose of this qualitative paper is to share Leads’ stories of policyenactment, particularly their approaches to fulfilling a provincial mandate that carries with it the legacyof historical and contemporary trauma and mistrust between Indigenous and non-Indigenous peoples. Theprinciples of Critical Narrative Research (CNR) combined with the sensibilities of Actor-Network Theory(ANT) are drawn on to foreground how Leads understand their own actions and interactions throughoutthe policy implementation process, as well as how they come to understand the actions, interactions, andintentions of other materialities of practice (e.g., professionals, standardized tests, curricula, bodies, androutines) within their milieus.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.057 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".