Bibliographic record
Abstract
After completing data collection, analysis, and interpretation, it is important to consider how you will share what you have learned with others. Creating a “space” for writing and identifying the “spaces” for sharing the outcomes of your research are critical. While one of the main reasons we conduct action research is to inform our current and future practice, and our understanding of that practice, it is equally important to share this knowledge with others. Cochran-Smith & Lytle (2009) describe the knowledge that practitioners generate through inquiry as local knowledge of practice. They reject the traditional notion that there are only two types of knowledge that inform our understanding of teaching - formal or professional knowledge and practical knowledge. Formal knowledge is generally considered that which is produced through conventional research by researchers; it is conceptual knowledge about education, teaching, and learning that has potential for generalization and meets the criteria for validity and reliability. Practical knowledge, in contrast, involves using one’s wisdom of teaching to make decisions and judgments in concrete situations that arise during the teaching process. This wisdom may often be tacit and not easily articulated. “Local knowledge of practice” then, is generated by action researchers working collaboratively in communities as they “theorize and construct their work” (Cochran-Smith & Lytle 2009, p. 131). It is relevant to the local context, but can also be publicly shared with many others, such as school-based colleagues, university-based educators and researchers, parents, K-12 students, and those in other professional settings. It is knowledge that can be “borrowed, interpreted, and reinvented in other local contexts” (p. 132). This chapter will discuss considerations and decisions that need to be made prior to sharing action research outcomes, as well as possible formats that may be adopted for dissemination.
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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 | 0.046 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.028 | 0.040 |
| Open science | 0.005 | 0.046 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.014 |
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".