Archivaria: The Journal of the Association of Canadian Archivists br. 86 (2018)
Bibliographic record
Abstract
U članku Looking for a Place to Happen: Collective Memory, Digital Music Archiving, and the Tragically Hip ("Tražeći mjesto događaja: Kolektivno pamće nje, arhiviranje digitalne glazbe i Tragically Hip") Alan Galey gradi studiju slučaja na primjeru kanadske rock grupe Tragically Hip.Problem arhiviranja popularne glazbe u razdoblju digitalne prolaznosti predstavlja izazov za arhiviste i povjesni čare, ali i amatere, koji su pokazali izrazitu zainteresiranost i angažiranost.Galey istražuje načine na koje je Tragically Hip konstruirao kolektivno pamćenje Kana đana jer je ono bilo pokretačka snaga za nastanak i razvoj arhivske zajednice.Tragically Hip imao je stalne improvizacije u nastupima uživo, a publika je te "nepredvidljive radionice" snimala uz potpuno odobrenje benda.Neautorizirane, neovlaštene i neslužbene snimke (tzv.bootlegs) mogu biti ilegalne i korištene isključivo za osobne potrebe, ali su u opisanom slučaju postale nekonvencionalni arhivski zapisi o popularnoj kulturi, koja bi inače bila izgubljena.Iako su arhivi smatrani pouzdanim mjestima za očuvanje dokaza, brojne su institucije zanema rivale takve zapise zbog neriješenoga pravnog statusa, osobito povrede prava inte lektualnoga vlasništva.Zahvaljujući profesionalnim amaterima, njihovu doku mentiranju koncertnih turneja, obradi, valorizaciji i distribuciji, stvorilo se novo mjesto arhiviranja izvan okvira tradicionalnih institucija.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.016 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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".