A Comparative Study of Teachers’Remuneration Systems between Republic of Korea and Canada
Bibliographic record
Abstract
이 연구는 한국과 캐나다의 교원보수체계의 비교를 목적으로 제도적 차원뿐만 아니라 교사처우와 교직에 대한 인식을 살펴보고 양국 현직 교사들의 직업관에 대해 알아보았다. 이를 위해 문헌 연구와 질적 연구를 병행하여 양국 교원보수체계의 지향점 및 이와 관련된 교사들의 인식을 분석•비교했다. 연구 결과 몇 가지 공통점과 차이점이 발견되었다. 첫째, 보수체계의 운영철학에서 한국은 국가 경쟁력 차원의 효율성을, 캐나다는 개인 위주의 능력계발을 중시했다. 둘째, 양국의 교원 보수체계의 공통적인 목표는 평등지향적인 합리적 교원보수제도의 확립이다. 그러나 평등에 대한 입장에서 한국의 경우 일반 공무원과의 형평성이 강조된 반면에 캐나다의 경우에는 성평등이 주로 거론되었다. 셋째, 교직에 대한 태도에서는 보수체계를 중심으로 두드러진 차이를 보였다. 한국의 경우 교직은 안정적이고 사회적으로 선망되는 직업이지만 진입장벽이 높고 보수체계가 상대적으로 경직되어 있었다. 반면에 캐나다의 경우 교직으로의 진입이 용이하고 보수체계가 유연하지만 이직 또는 겸직에 대한 고려가 빈번하고 사회적 지위가 유동적이라고 인식되었다.This study examines the social recognition of teachers’ treatment and teaching profession through the comparison and analysis of the teachers’ remuneration systems in the Republic of Korea(hereafter, Korea) and Canada. For this purpose, literature review and qualitative research were conducted. As a result of comparing and analyzing the orientation of teachers’ remuneration systems in both countries and perception of teachers, the following differences were found. First, in terms of the management philosophy of the remuneration system, Korea emphasized the efficiency of the national competitiveness dimension, while Canada focused on the interrelationship with development of individual-oriented competency. Second, although the remuneration systems for teachers in both countries are quite different, they have aimed at establishing a reasonable remuneration system, which considers equality in common. But the position on equality was different between the two countries. In the case of Korea, equity was considered by comparing the pay scale with those of other government employees, while equity in Canada mainly had to do with gender equality. Third, the teachers of both countries regarded the sense of duty and ethics as important qualities of the teaching profession, and they recognized the social safety net as an indicator of their social status. However, there was a difference in attitude toward the teaching profession. In Korea, it was deemed to be a stable and socially desirable profession, but the entry barriers were quite high and the remuneration system was relatively rigid. In Canada, on the other hand, it was easy to enter the teaching profession and the remuneration system was far less rigid. But teachers were more likely to quit or take a second job, and their social status fluctuated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".