Going Beyond: Implementing “Beyond Quality” through the Investigating Quality (IQ) Project
Bibliographic record
Abstract
The Investigating Quality (IQ) Project was conceptualized as a multiple systems approach to transforming early childhood education, care, and development (ECE/ECD) in British Columbia, Canada. Those systems extended from provincial government to local programs, from innovative policies to new approaches to practice. Fortunately, the tenure of the IQ project (16 years in 2021) allowed time for research and scholarly work to take place that ranged from comparative policy analysis to analyses of frontline practice authored by practitioners as well as academics, and from innovations in ECE post-secondary education to the study of environmental and equity issues within the Anthropocene. The IQ project’s publication outlets included international scholarly journals as well as provincial and national ECE association journals. Among the scholarly works were many that commenced as master’s theses and doctoral dissertations, themselves opening up new avenues of study. While key inspirations for the IQ Project (including Indigenous initiatives in Canada) predate and lie outside the initial, early 1990s foci of the U.S.-initiated ECE Reconceptualist movement (RECE), later international RECE, as well as Reggio-Emilia, Swedish/Reggio, and Aotearoa/New Zealand Te Whariki scholarly literatures, are kindred spirits for IQ articles, chapters, and books.
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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 | 0.131 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.038 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".