What Does `Evaluationź Mean for the NIME Community?
Bibliographic record
Abstract
Evaluation has been suggested to be one of the main trends in current NIME research. However, the meaning of the term for the community may not be as clear as it seems. In order to explore this issue, we have analyzed all papers and posters published in the proceedings of the NIME conference from 2012 to 2014. For each publication that explicitly mentioned the term we looked for: a) What targets and stakeholders were considered? b) What goals were set? c) What criteria were used? d) What methods were used? e) How long did the last? Results show different understandings of evaluation, with little consistency regarding the usage of the word. Surprisingly in some cases, not even basic information such as goal, criteria and methods were provided. In this paper, we attempt to provide an idea of what evaluation means for the NIME community, pushing the discussion towards how could we make a better use of on NIME design and what criteria should be used regarding each goal.
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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 | 0.209 | 0.327 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.053 | 0.042 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.010 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".