Gambiarra and Techno-Vernacular Creativity in NIME Research
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it