MétaCan
Menu
Back to cohort
Record W2338958948

What Does `Evaluationź Mean for the NIME Community?

2015· article· en· W2338958948 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNew Interfaces for Musical Expression · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Society and Technology Trends
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsComputer scienceTerm (time)Consistency (knowledge bases)Meaning (existential)Set (abstract data type)Order (exchange)Management scienceData scienceEpistemologyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.398
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it