ECO NOM IC I NTEREST GROUPS I N CANADA ECO NOMIC INTEREST GROUPS IN CAN ADA
Bibliographic record
Abstract
ii The question deal t wi th in this thesis is the 8xi~;te nc8 of interest groups within the Ca nad ian economy. An ex'-~minC1t.i()n of the literature shows that, although this concopt has only b88n applied in one study of the Canadian economy, numerous authors have discussed the formation, general characteris.L.ic~), and effect of i nterest groups within t he economy of other countries, especially the Unit ed States. Fr om these analyses~ a definition of interest group is constructed. J-in intere~)t group is defined as a set of industrial corporations and financial insti tut io rls LJilich are all.i.ed in a number of uays, (for example, by interlocki n g directorates and ownership, us e of th e sa ~8 brokerage company, corporate law firm, bond trustee, tr a nsfer agent, and by historical affiliation), in their struggle to maximize profits, expand m~rkets, and increase their capit. d.l supply_ Previous research indic ates that the main advantages of p:':n'tici~J?tion lJUhin 5Jtl'l interest g:rourfnI'e i ncrease d profi ts and ( ~J ' r).. L\\) ", \\ """Cl; ~ ~ 31's ris l<). Th g int.erest group reduces conflict behl8ert mefilb~,r~) of th;::!Jrl)U ~ it.sGlf ~ but also promot.es confJict hetween groups ~or unaffiliated companies and markets. Th e '3fT!pirical study focuses u[J o[1 the relationship betw 89n a selected sample of majdr industrial corporations and f inancial inst.itutions, and the five largest Canadian banks- ···
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".