Probing the complexities of collaboration and collaborative processes
Bibliographic record
Abstract
System leadership is interested in building staff capacity and student academic improvement. The literature suggests that if we are going to positively impact student achievement as an outcome, teaching within professional learning communities requires skilled and engaged collaborators. This qualitative study has intentionally sought to better understand the complexities involved in collaboration through the lens of nine teachers and four administrators from five different school boards. The methodology included the use of semi-structured interviews and a constructivist framework. The study's participants shared their understandings and experiences regarding collaborative work. It appears issues of engagement, trust and professional relationship are critical to developing collaborative processes that motivate teachers. The study's findings highlight complexities of collaborative work that includes how cultural experience influences our assumptions regarding collaboration. As well, a tension exists between espoused values about collaborative work and what participants report as actual collaborative effort. This study contributes to literature that probes the role of emotions in schools as workplaces---or as Andy Hargreaves suggests, the emotional geographies of teaching. Our work with other educators appears to evoke a range of emotional responses from optimism and hope to resentment and a sense of betrayal. The structure, form and content of collaboration are important, however the relational trust within the collaboration appears to be the glue that binds people, purpose and outcomes together. Effective collaborators appear very skilled in active listening, in leading dynamic conversations, in facilitating and guiding collective inquiry. Finally, the role of leadership cannot be underestimated as leaders make important connections with others as co-collaborators and make important connections for others in illuminating what is essentially a moral purpose in our efforts to improve schools. Recommendations for practice and further research include the need for leaders to become skilled and critical coaches of collaborative work as well as skilled facilitators who empower others to become more reflective and collaborative.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".