Bibliographic record
Abstract
This article seeks to inform plans of the building of infrastructure for the balanced development of winter sports. This study was conducted in the following four phases: first, 7 sports of the 15 winter sports were selected and as of 2010, the present condition of registered athletes, national athletes, reserved athletes coaches, athletic performance, facilities and finance in the 7 sports was investigated; second, a total of348 experts involved in sport were surveyed in order to acquire a wide range of knowledge and data relating to the development of winter sports; third, cases of programs relevant to the development of winter sports in Canada and the United States were examined; and fourth, seven different plans of the building of infrastructure for the balanced development of winter sports drawn from the above three phases of the study are as follows: first, a successful bid and hosting of the 2018 Pyeongchang Winter Olympics; second, the creation of an integrated exclusiveagency for winter sports; third, the establishment of an integrated information service for winter sports; fourth, the development and spread of winter e-sports and u-sports; fifth, the expansion of the voucher service of winter sports (i.e. Dream program inand outside the country); six, the construction of an innovative winter elite sports system: the normalization of winter school sports; and seventh, the security of a stable financial base: Alpensia Resort vs. Everland.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".