실업급여의 수급자격과 구성체계 -카나다와 한국의 고용보험법을 기초로 한 비교연구-
Bibliographic record
Abstract
This Article attempts to establish long-term goal for improvement of\n\nUnemployment Benefits(UB) in the Korean Employment Insurance Act(KEIA) by\n\ncompare the difference of UB between in the Canadian Employment Insurance\n\nAct(CEIA) and in KEIA. The author chooses statutes of CEIA and KEIA as\n\ncomparative basic materials for clarifying the difference of UB. He limits the\n\nscope of comparative study to two categories of entitlement and types of UB.\n\nFirstly, the author points out the differences in two critical requirements of\n\nentitlement of UB; just cause for voluntarily leaving employment or being not\n\nmisconduct for dismissal and efforts to search for suitable employment. In\n\nCEIA section 30(1) states that a claimant is disqualified when he or she quits\n\nwithout just cause or when he or she is fired due to his or her own misconduct,\n\nand section 50(8) requires that a claimant prove he or she is making reasonable\n\nand customary efforts to obtain suitable employment. 1) Just cause is generally\n\ndefined under section 29(c) of CEIA as having regard to all the circumstances,\n\nthe individual had no reasonable alternative to leaving the employment, and\n\nsections 29(c)(i)-(xiv) of CEIA specifically provide a list of the circumstances\n\nthat can constitute just cause. Contrary to CEIA, in KEIA there is no general\n\ndefinition provision of just cause and a specific list of it. 2) Misconduct is\n\nnot defined in CEIA and KEIA, therefore the word must be commonly given its\n\nmeaning by legal interpretation. 3) In KEIA there is no counterpart of suitable\n\nemployment of CEIA.\n\nSecondly, he also analyses the differences in the structure and the types of...
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 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 teacher head, 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".