Bibliographic record
Abstract
Prve teoretske zapise o spominu najdemo v hipokratskih spisih; z njim sta se ukvarjala tudi aleksandrinska zdravnika Herofil in Erazistrat ter pozneje Galen. Prva prava znanstvena razprava o spominu je bil Aristotelov spis O spominu in spominjanju; večina piscev pred njim se je s spominom ukvarjala zgolj sporadično. Posebno poglavje, ki je v antiki tesno povezano z obravnavo spomina, je mnemotehnika, ki je imela velik pomen predvsem zaradi retorike. Med latinskimi pisci sta se s spominom ukvarjala predvsem Ciceron in Kvintilijan. Med številnimi komentatorji Aristotelovih del sta komentarje k spisu O spominu in spominjanju napisala samo dva: Psevdo-Temistij (Sofonias) in Aleksander iz Afrodiziade. Pomembna vezna člena med antiko in srednjim vekom sta bila Avrelij Avguštin in zlasti Boetij. V latinščino so Aristotelova dela, tudi zbirko Parva naturalia, prevedli v 12. stoletju. Med prevajalci izstopata Jakob iz Benetk in še posebej Willem van Moerbeke. Pravi zagon Aristotelovemu nauku o spominu pa sta s svojim delom dala arabska učenjaka: Avicenna (Ibn Sina) s Knjigo o duši (Liber de anima) in Averroes (Ibn Rušd) s komentarjem-kompendijem k Parva naturalia; Avicennovo delo je v 12. stoletju v sodelovanju z Dominikom Gundissalinom v latinščino prevedel Avendauth (Ibn Daud), Averroesov kompendij k Parva naturalia pa v 13. stoletju Mihael Škot. Na podlagi Avicennovega in Averroesovega dela je nastalo tudi nekaj anonimnih razprav, ki se ukvarjajo s spominom, z vprašanji spomina pa sta se ukvarjala tudi francoski frančiškanski teolog Jean de la Rochelle in Guillaume de'Auvergne. Povsem novo poglavje pri obravnavi spomina nastopi z delom Alberta Velikega in Tomaža Akvinskega.
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.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.338 | 0.170 |
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".