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Record W4384400578 · doi:10.5539/hes.v13n3p101

Analysis of Legal Risks in Psychological Crisis Events among University Students

2023· article· en· W4384400578 on OpenAlexvenueno aff
Xiaochuan Guo

Bibliographic record

VenueHigher Education Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsComplaintStatutory lawPublic relationsCorporate governanceDutyPsychologyPolitical scienceHigher educationProcess (computing)Legal processDiversity (politics)LawBusiness

Abstract

fetched live from OpenAlex

The psychological crises of university students are an important factor affecting the security and stability of colleges and universities. Against the background of school governance, the definition and characteristics of college students' psychological crisis events should be correctly understood and the legal risks clearly classified to help prevent crisis incidents. Legal risks have important practical significance. The author starts with the definition of college students' psychological crisis events and divides the characteristics into two parts: cause diversity and result sensitivity. The legal risks associated with the process are clearly divided into civil, administrative and procedural legal risks, which are specifically manifested in the duty of care in the prevention and response stages of the safety responsibility of colleges and universities. In the process, is there any infringement of privacy violations, and when an incident occurs, does the university take timely and positive measures and pay attention to protecting citizens’ legitimate rights and interests during the emergency response process? Are statutory after-care obligations such as diversion and guidance covered in the aftermath phase? Are students provided with reasonable and legitimate complaint channels?

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.080
GPT teacher head0.425
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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