The Theory of Unrealistic Expectations: Utilizing a Framework of Established Mass Communication Theories to Develop a New Perspective on a Current Communications Challenge
Bibliographic record
Abstract
Mass communication theories are utilized to shed light on a challenge faced by the Canada Revenue Agency. The author leverages established theories to propose a new one – The Theory of Unrealistic Expectations. He explains that if people can see a network, they are far more likely to have realistic expectations about how long it will take for them to move through the queue or get service. For example, people who use highways that are jammed with traffic on holiday weekends know that their journey will take a long time and they will likely make an accommodation by leaving early. That is not the case when the network can’t be seen. The author uses this theory to reflect on why many people who file their taxes online do so at the last minute, thus creating challenges for the Canada Revenue Agency.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".