Bibliographic record
Abstract
Bu yaz kapsamnda bata gerceklik olmak zere, kavramlar ve kavramn rettigi bir alan olarak eylemler ve bu alann icinde nelerin yer alacag ve alamayacag, nelerin mekana nesne olabilecegini ve olamayacagn belirleyen etkenler ele alnacaktr.Varolan her eyin anlam ve degeri, onlarn ortaya kardklar sonuclarn toplamdr. Nesne alglayan bir varlktan bagmsz, salt fiziksel zellikler zerinden; belli bir agrlg, kitlesi, oylumu, rengi, maddesi olan her trl cansz varlk olarak, sadece alglananlarn oldugunu, alglanmayann olmadgn syleyen felsefi akmlar zerinden tanmlanrsa znenin, kiinin dnda kalan, d dnyann bir parcas olarak bilincin karsnda duran her konu, her ey olarak tanmlanabilir. Tecrbe sonucu dogru yarglara ulamak ya da yanl olan yargy dzeltmek icin kendisiyle etkileime girilen bir arayz olarak mekan istisnasz olarak her zaman alglayan varlklar olmasa da mekan diye bir gercek varolabilirmi gibi tasarlanr. Modern dnemin tasarmcs icin tasarlama eylemi; bir ilev-estetik rnts oluturanya da oluturmak zorunda oldugunu varsaydg -kavramlar arasndaki tutarllg saglamaktr. Bu tutarllg saglamayan
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
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".