Using Social Learning Spaces to Think Beyond and Innovate Conventional Conferencing Formats
Bibliographic record
Abstract
Across the academic landscape, scientific organizations host conferences that enable researchers to come together to foster learning, stimulate innovation, and promote change. Within the diverse field of kinesiology, conferences can help develop and disseminate knowledge on a range of issues such as athlete development and coach education. The purpose of the present article is to discuss the possibilities of thinking beyond conventional conferencing formats by creating dynamic social learning spaces that promote networking, critical thinking, and reflexivity. The theory underpinning social learning spaces is explained, followed by a narrative chronology of the three phases of evolution of the blue room group , an interdisciplinary collaboration of youth sport scholars who aim to foster innovation across subdisciplines of kinesiology. An interpretative summary of the blue room group as a social learning space is presented, in accordance with the principles of caring to make a difference, engaging uncertainty, and paying attention. The perceived benefits of kinesiology, as well as the challenges and limitations of the blue room, are discussed based on the authors’ experiences operating within a continuously evolving and shifting social learning space.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".