On the Values of Travels Literature and its Development & Utilization
Bibliographic record
Abstract
Travels literature is employed to record and describe the author's travel experience, impression and feeling which is in prose style. Its component factors and expressive means composed elements determine its multiple value system, which contain the values in literature, aesthetics, science, history, idea and politics. In order to better developing and utilizing the travels literature resources, we need systematical assortment, care arrangement and combination ,gradual realization of the digital travels literature. Keywords: Travels, Literature study, Tourism Resume Le recit de voyage est un genre de document qui consiste a narrer et decrire par la prose ce que l’auteur voit, entent et ressent durant son voyage. Ses facteurs constituants et faccons d’expression determinent ses multiples systemes de valeur qui s’expriment comme suite : valeurs litteraire et esthetique, valeurs scientifique et historique, valeurs de pensee et politique. L’exploitation et l’utilisation du recit de voyage doit commencer par le recensement global des documents de voyage, l’arrangement et la combinaison des ressources d’informations touristiques et la realisation progressive de la numerisation du recit de voyage. Mots-cles : recit de voyage, recherche du document, voyage 摘要 遊記是以散文形式記敍、抒寫作者親身旅行遊覽見聞感受的文獻類型，其構成要素與表達方式決定了多重的價值體系，主要體現為文學與美學價值、科學與史料價值、思想與政治價值。對遊記的開發利用應從全面普查遊記文獻、整合遊記資訊資源、逐步實現遊記的數位化入手。 關鍵詞：遊記；文獻研究；旅遊
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.023 | 0.031 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".