Bibliographic record
Abstract
The purpose of this project was to design a tool to identify teachers' perceptions of where they are in their Professional Learning Community (PLC) venture. I employed a quasi-qualitative research approach. I developed a survey tool which was applied in an elementary school setting to assess its usefulness. The survey tool used a Likert-like scale that drew on statements similar to those employed by Hipp and Huffman (2003) to illuminate seven dimensions of a PLC community. These dimensions included: Shared and Supportive Leadership Shared Mission, Vision and Values Collective Inquiry Collaborative Teams Supportive Conditions - Relationships Supportive Conditions - Structure, and Data Based Decision Making. Data from the survey, comments on the survey, and a focus group were used in the analyses and discussion of where teachers in this particular school are in their PLC journey. The results indicated a strong level of agreement to the statements identifying the various components of a PLC. However, the survey also illuminated a number of areas of concern that when addressed by the school administrative team will lead to a more inclusive and sustainable PLC in this school. --P.ii.
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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.023 | 0.047 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".