Bibliographic record
Abstract
b J oh n s ton W e want to tell you about a collaboration with which we have been involved, 1 among geographers, theatre artists, and community activists.Collectively, we transformed conventional research interview transcripts into a play, performed in 2009, first at the PuSh International Performance Arts Festival in Vancouver and then at the "Your Nanny Hates You!" Festival in Berlin.We did this not only to disseminate research but also to work with the affective power of words and staging in a theatrical setting.Two events, only weeks before our second development workshop in July 2008, heightened our appreciation of the complexity of the challenges we faced in doing this.When we expressed to our dramaturge, Martin Kinch, a desire for minimal theatricality, he responded: "If you want the text to speak for itself, why not just publish it?Why move to performance?"He also said: "When you go into the realm of live performance you can't bank on the compelling nature of words."The following week, the University of British Columbia Research Ethics Board asked: "Do you have permission to use the material from the earlier study [to create the play]?It can only be used again with permission from the original study.If you did not receive consent [from those you interviewed] to use the data in future studies you cannot use it [in this way]."The issue was eventually resolved when the play was conceived as a "knowledge translation initiative."Like any translation, ours was far from straightforward: the protocols of research line up imperfectly with those of theatre, and live performance is so much more than words.The title of our play, Nanay, is Tagalog for "mother" but also suggests the English word "nanny."Thus, it intimates both the possibilities and uncertainties of cross-cultural communication, and it is these, as well as the complexity, interest, and excitement involved in translating interview material into a play, that we take up here.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".