Bibliographic record
Abstract
資訊傳播科技的快速發展帶來許多新的資料類型，而資料類型標示(General Materials Designations '簡稱GMD) 的著錄、呈現樣式及檢索功能更成為數位時代編目規則的熱門議題。1997 年在Toronto 舉辦的英美編目規則原則及未來發展的國際性會議(Intemational Conference on the Principles and Future Development of AACR) 中， Tom Delsey 指出GMD 在分類邏輯上的不一致性，各種資料類型標示 的特定性並非處於同一等紋，參雜混合資料內容及載體的類型描述。Toronto 會議引發各界對傳統GMD 的重新檢視及AACR2R 規則0 .24 的修訂。2006 年，取代AACR 的新內容標準RDA(Resource Access and Description) 草案對GMD 有相當大幅度的修改，推出全新的資訊資源、分類絮構。筆者研讀相關文獻，試從GMD的歷史發展探討資訊資源類型標示的分類及其未來。結語指出或許新的資訊資源分類方式比舊版規則更合乎邏輯，但是不夠明確簡易。從各界對RDA 相關規則(3.2 ，4.2) 的評論，可見離新規則的使用還有一段很長的路要走。
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".