Clarity of the Educational Assistant Role: A Look toward Policy and Practices
Bibliographic record
Abstract
As an Educational Assistant (EA) working in an urban Ontario School Board, I aim to explore my ontological and epistemological views on policy implications that impact my work.Over the past two decades, I have witnessed the evolution of the EA role from providing academic and one-to-one developmental support to managing multiple student caseloads with the most challenging school needs.While the demands have increased, my concern is that the voice of EAs needs to keep pace with the changes in the role.From my position through a reflexive self-study and literature review, I examined my frontline experience, detailing anonymous work experiences related to the language within the Ontario R.R.O.1990, Reg. 306: Special Education Programs and Services using the theoretical lens of critical policy theory.A literature review found data specific to Special Education and the marginal frontline employee voice.The critical policy theory framework supported an in-depth examination of the relationship between meaning making within policy and practice outcomes.The self-study method allowed engagement with my experiences and research findings to advocate for authentic policy for a changing role.
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 | 0.087 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.055 |
| Scholarly communication | 0.033 | 0.017 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".