Bibliographic record
Abstract
Introduction: according to the lobbying legislation in Canada, lobbying activity describes as a legitimate way to influence on the decision making process. The article contains analysis of lobbying regulation at the Federal and the provincial levels of Canada. Also it includes the Codes of conduct for lobbyists. Purpose: author sticks to the point that lobbying legislation in this country forms one of the most effective modern models of legal regulation of lobbying. Results: the canadian legislators explain lobbying as a legal part of the political democratic process. Author turns to details of their regulations, definitions of lobbying, public office-holders, lobbyists, describes penalties, circumstances and rules of registration. There are two types of lobbyists in Canada lobbyists-consultants and corporate lobbyists. Author describes the procedure of registration for all types of lobbyists and provides updated statistics of registered lobbyists. Analyzes amendments to the federal lobbying act helped clean up loopholes in regulation. This article provides penalties for illegal or improper lobbying. Conclusions: the Canadian model of legal regulation of lobbying refers to medium-regulated. It describes effectiveness of the Canadian system and possibility to use some of the features to create system of legal regulation of lobbying in Russia.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".