Play space – head space – third space: playful pedagogy and research – ways of building collaborative and creative communities of learners
Bibliographic record
Abstract
This bricolage untext constructs a meditation on third space professionals, practices, and opportunities. We, the authors, have reflected on our own past contributions on the topic – and contributions from some friends and allies – in blogs and articles, in books and activities – and playfully selected those that at this moment we like the best, that we find the most provocative, intriguing, or useful. To carry third space practice further, rather than writing a summative reflective piece drawing together our thinking in a suitably formal and dense academic piece, we have cut up what we have written – we have blacked out the blogs and PowerPoint sessions of others – and we have put these together to create a new story: that explores the creation of 'third spaces' and immersive activities as pedagogical practices for powerful student learning. A story, as Jean Luc Goddard would say – with a beginning, middle, and end – but not necessarily in that order. Thus, our text is an untext and an unspace – a metonym, a synecdoche, a provocation.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".