Message Manipulation: How Downsizing Messages are Encoded Based on the Intended Audience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it