A List of Questions About How We Ask Questions Some Thoughts on KMb and Theatre
Bibliographic record
Abstract
The mixed case acronym, KMb, with its capital ‘KM’ and lower case ‘b,’ confuses my brain, conjuring some kind of Seussian unit of measurement like kilometres per megabyte. Upon second thought, perhaps this is not entirely so surreal, since KMb is indeed about making those information units go places and speedily. A relatively recent addition to scholarly parlance, ‘knowledge mobilization’ (KMb) came into general usage following the creation of the Community University Research Alliances (CURA) program of the Social Sciences Humanities Research Council of Canada (SSHRC) in 1999 (“History”). Knowledge mobilization refers to processes that facilitate the sharing of research between research producers (usually university academics) and research users (usually community organizations). Aspiring to be more than merely a one-way transfer of knowledge, KMb also seeks to foster entry points through which knowledge users can proactively pitch problems to be taken up as research projects. Likewise, KMb aims to be collaborative, creating opportunities for academics and community partners to work together to define the problem, develop the methodology, collect data, and apply and assess the results. Explicitly through SSHRC and tacitly by other funding bodies, we are being encouraged (compelled?) to bring these community-oriented values and goals to the fore. My intent then in this brief “think piece” is to present a long list of questions, the answers to which, I hope, may in a small way contribute to our work in imagining what the ongoing development of KMb in theatre studies and theatre practice will look like.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".