Gambiarra and Techno-Vernacular Creativity in NIME Research
Bibliographic record
Abstract
Over past editions of the NIME Conference, there has been a growing concern towards diversity and inclusion.It is relevant for an international community whose vast majority of its members are in Europe, the USA, and Canada to seek a richer cultural diversity.To contribute to a decolonial perspective in the inclusion of underrepresented countries and ethnic/racial groups, we discuss Gambiarra and Techno-Vernacular Creativity concepts.We believe these concepts may help structure and stimulate individuals from these underrepresented contexts to perform research in the NIME field.similar to race, social similarity, or religion, as shown by a study in the USA [5].In a global community such as NIME, the concept of ethnicity and race is undoubtedly even more complex and should be a topic for further discussion.Nevertheless, it is alarming that no one was from the African continent or wrote anything related to African descent.Surveys are helpful tools to help us understand how we can improve diversity in many ways.Efforts to broaden its range and improve its precision will certainly direct our community for the better.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".