POOR COMMUNICATION IS AL IVE AND WELL: A STUDY OF ANNUAL REPORT READABIL ITY
Bibliographic record
Abstract
Annual repor ts p rov ide t he p r ima ry communication medium between corporat ions and t he i r important external audiences, notably t he i r shareholders. Th i s s t u d y applies Flesch and Fog formulas t o demonstrate t h a t Canadiar? corporate annual repor ts a re p repared t o degrees o f reader comprehension d i f f i cu l t y beyond t he educational attainment levels o f 90 % o f a l l Canadians, and two- th i rds o f Canadian shareholders. Les rappor ts annuels sont le p r inc ipa l v6hic le de communication en t r e les corporat ions e t la populat ion concernCe, notamment leurs actionaaires. Cet te dtude a recours aux modsles de Flesch e t Fog, a f in de p rouve r que, les rappor ts annuels des corporat ions canadiennes sont prCparCs a u n niveau de connaissance supdr ieur a ce lu i de 9 0 pour cen t des Canadiens, e t d e deux- t iers des act ionnaires canadiens. In t roduc t ion Annual repor ts a re a p r ima ry means b y which corporat ions communicate formally w i t h t he i r shareholders, investors, c red i to rs and t he general publ ic, and unless t h i s specialized communication medium can be b road ly understood, a t least some readers w i l l have d i f f i cu l t y a r r i v i n g a t rat ional investment decisions. Insofar as low levels o f annual r epo r t comprehension p roduce resource misallocation behaviour,
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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 | 0.006 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".