Message Manipulation: How Downsizing Messages are Encoded Based on the Intended Audience
Bibliographic record
Abstract
This research explores the differences between how organizations communicate downsizing messages to external receivers (e.g. consumers, shareholders, etc.) versus internal receivers (e.g. employees). This study uses a unique dataset of 145 mass layoff forms submitted to the Ontario Ministry of Labour, Training and Skills Development (OMLTSD) from 2013-2019, and the accompanying downsizing announcements made in the media. Linguistic Inquiry and Word Count (LIWC) text analysis software analyzed message formality, deception, confidence, emotional tone, and information quantity for downsizing announcements to both audiences. T-test analysis determines significant differences between these announcements. Downsizing messages communicated to internal receivers are more formal, confident, and succinct while downsizing messages communicated to external receivers are more deceptive and have a more negative emotional tone. This study uses an interdisciplinary approach (blending marketing and human resources management disciplines). In this study, the communications model is applied to organizational communication. Further, this is the first study to compare the same downsizing messages that were communicated to different audiences. Finally, this study uses a distinctly unique dataset to better explore and understand this complex topic.
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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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".