Bibliographic record
Abstract
This guide covers over 2,000 of the known professional designations and designatory letters in accounting, auditing, finance, investment, insurance, logistics, management, purchasing, marketing, actuarial science, consulting, corporate governance, information technology, information technology security, supply management, banking, real estate, taxation, inventory management and other related disciplines. Over 980 professional bodies from 147 countries around the world are in this handy reference. This directory can be used to track organizations around the world to provide further material on the programs leading up to the qualifications they offer which are listed here. The descriptions obtained can then be evaluated to measure their worth, coverage, and in-depth level to determine comparability for peer acceptance. The book is organized alphabetically by countries. Each entry listed has 11 fields as follows: Designation; Address; Designatory Letters; Telephone; Administering Body; E-mail; Membership; Fax; Profession; Web; Established. This chapter includes the information for the following countries: Cameroon, Canada, Chile, China, Colombia, Costa Rica, Cote D'ivoire, Croatia, Cuba, Cyprus, and Czech Republic.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.393 | 0.117 |
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".