Analysis on the Undergraduate Fate of Archives Major and Its Countermeasures —— Taking Yancheng Teachers University as an Example
Bibliographic record
Abstract
With the rapid development of society and economy and the continuous popularization of higher quality education, the employment pressure of University students has become more serious. Among them, the practical and theoretical strong undergraduate graduates of archival science majors face more intense employment competition. This article mainly starts from the general employment forms of undergraduate students majoring in archival science in the context of the general environment. Through the employment of undergraduate students majoring in archival science in 2009-2019 (including the suspension of archival majors in 2010, 2011 and 2014), Examination of the public rate, the professional ratio of archives directly engaged in work, and other aspects of statistical data analysis programs, to explore the employment prospects of archives graduates, and the main factors affecting the employment status of archives majors and propose corresponding countermeasures. Based on the analysis of the impact on the employment status of archives graduates, and combined with the data of the employment history of archives in Yancheng Teachers University, this paper puts forward suggestions to improve the quality of the undergraduate of archival science.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".