Defining and exploring pracademia: identity, community, and engagement
Bibliographic record
Abstract
Purpose The aim of this paper is to define pracademia and conceptualise it in relation to educational contexts. This paper contributes to and stimulates a continuing and evolving conversation around pracademia and its relevance, role and possibilities. Design/methodology/approach This paper is a conceptual exploration. It draws upon existing and emerging pieces of literature, the use of metaphor as a meaning-making tool, and the positionalities of the authors, to develop the concept of pracademia. Findings The authors posit that pracademics who simultaneously straddle the worlds of practice, policy, and academia embody new possibilities as boundary spanners in the field of education for knowledge mobilization, networks, community membership, and responding to systemic challenges. However, being a pracademic requires the constant reconciling of the demands of multi-membership and ultimately, pracademics must establish sufficient legitimacy to be respected in two or more currently distinct worlds. Practical implications This paper has implications for knowledge mobilization, networks, boundary spanners, leadership, professional learning, and connecting practice, policy, and research. While the authors are in the field of education, this exploration of pracademia is relevant not only to the field of education but also to other fields in which there is a clear need to connect practice/policy with scholarship. Originality/value This paper provides a new definition of pracademia and argues that pracademia identifies an important yet relatively unknown space with many possibilities in the field of education.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.069 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".