Bibliographic record
Abstract
Purpose Drawing from findings of a case study of inter‐organisational collaboration, this paper aims to employ organisational theory to examine the potential learning that opens between educational organisations. The focus is discursive practices. Two questions guide the analysis. What (unique) practices are implicated in the “knotworking” of inter‐organisational collaboration? What knowledge and capacities are learned in these discursive practices? Design/methodology/approach A case study was conducted of a collaboration between a university unit, school district, elementary school and parent executive board to govern a laboratory school. Documents were examined and 17 interviews conducted and analysed inductively. Document analysis and second stage transcript analysis employed methods of discourse analysis. Findings The case analysis suggests that collaborations open unique sites for organizational learning. Actors (teachers, administrators, parents) engage with various discourses in the “knots” of inter‐organisational networks. Those who thrive in the “knot” of collaboration learn how to be flexibly attuned to shifting elements that emerge in negotiations. Further, these actors appear to develop capacities of mapping, translating, rearticulating, and spanning boundaries among the diverse positions of organisations. Research limitations/implications The case study is limited in scope in order to allow in‐depth discourse analysis of the data. Originality/value The combination of theories employed here – a practice‐based organizational learning theory called “knotworking” and critical organisational discourse analysis – is unique in educational administration research. It is argued that together, these theories provide a useful analytic approach for administrators wanting to understand and work through the cultural and political complexities of inter‐organisational collaborations.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".