Introduction: Everything New is New Again (and again, and again...)
Bibliographic record
Abstract
We first discussed the possibility of co-editing an issue of Theatre Research in Canada with a focus on the topic of intermediality back in June of 2006. While we knew that TRiC’s then lengthy list of guest-edited theme issues-in-waiting pushed the prospect several years into the future, we were both keen to explore the potential for such a project. As our turn in the limelight approached, a (to us) surprisingly small number of responses to our first two calls for proposals further delayed our move to print. However, the unusual time span between inception and completion has offered us a similarly uncommon opportunity to consider and revise our aspirations for the issue. Now, in the autumn of 2011 and on the verge of publication, evidence of this extended and somewhat circuitous evolution can be seen in this issue’s contents. One of the essays in this collection was first proposed to us in the middle of 2008; another only completed its peer-review process a couple of months ago. Charting the developments from the first conversations around the project, through the sequence of submissions, assessments, and revisions, it is easy to see that the field of intermedia studies has evolved rapidly and substantially during this period, with the result that the issue also provides an unintentional yet illuminating window on that evolution.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.021 |
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".