Research on the Informationization Construction of College Admission Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2014, the State Council issued the “Implementation Opinions of the State Council on Deepening the Reform of the Examinations and Admission System” (National Development [2014] No. 35), which made the most administrative framework for the reform of the examination and admission system on “promoting a fair and scientific selection of the talented”. The selection mode will be transformed from a single college entrance examination score to a multi-evaluation mechanism based on the college entrance examination, high school grades, and reference from comprehensive quality evaluation. The essence of multi-evaluation is mainly through qualitative or quantitative evaluation of students’ values, learning ability, innovative ability, critical thinking, and other aspects. The traditional college admission system simply cannot meet the requirements of reform. It is urgent to establish a comprehensive evaluation system including enrollment planning, propaganda, admission, and personnel training. This process requires massive-information-processing, and we must establish a suitable college admissions management information system with strong data analysis ability in order to provide technical support and decision support for college admission management.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it