A discourse analysis of the College of Early Childhood educators communications: how do they shape the professional identity of Ontario's registered early childhood educators?
Bibliographic record
Abstract
The College of Early Childhood Educators (CECE) regulates registered early childhood educators (RECEs) in Ontario, Canada. The CECE distributes numerous communications to RECEs, whereby the text (both implicitly and explicitly) works to situate ECEs within a particular professional identity. This research study applies discourse analysis to code and categorize text from 66 communications disseminated by the CECE to RECEs. I identify five key discourses as well as several discursive strategies used to reinforce the discourses that contribute to the construction of a professional identity for Ontario RECEs. This study also employs two theoretical frameworks, feminist theory and critical race theory (CRT), to examine “what is not being said” by the CECE about the realities of RECE working conditions. I offer a counter-discourse to provide a narrative account of how particular RECE working conditions and real life professional experiences collide with the five discourses, and create a professional crisis in a current patchwork system. Keywords: professional identity, discourses, constructionism, feminist theory, critical race theory (CRT)
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".