COMPARATIVE ANALYSIS: PROTECTION OF TCES (TRADITIONAL CULTURAL EXPRESSIONS) OF USA, FRANCE, CANADA, UK, JAPAN, KENYA, PHILIPPINES, PANAMA WITH INDIA
Bibliographic record
Abstract
A common consensus which the majority would settle on “The past deserves to be preserved”, it stays in the memories of shared human experiences within these numerous indigenous communities and of individuals passed down from generation to generation and is externalized in diverse forms, expressions, traditions and rituals; one of such artistic mediums[1] is known as Traditional Cultural Expressions.[2] The key aspects for defining TCEs would include a) Presence of cultural value, which evolves overtime[3] b) Communal essence, the work cannot be attributed to only one individual, and c) dissemination through generations whether orally or through imitation. The system is a tangled web of cultural values and heritage representing a collective identity whereby the learning is transmitted from generation to generation within the same group.[4] Unfortunately, the authentic content has become a target for commercial misappropriation, whether distortion, mutilation or straight up duplication without recognition which has created waves amongst the legal fraternity and stakeholders of the heritage. Art revolutions have been heavily influenced by cultural exchanges in the past and will continue to inspire change, which is why its protection is indispensable in the current age of transmission.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".