Recycling firm-generated content on social media platforms: phenomenon and research propositions
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
Purpose The manuscript aims to introduce the managerial practice of content recycling – that is, a firm's recycling of its posts on social media platforms. I define and distinguish the phenomenon from related ones and offer propositions for future research to test empirically. Design/methodology/approach Review of the practitioner literature, in-situ observations with content managers, and a survey of content managers and Facebook users. Findings Managers recycle their posts to recoup the costs of content. Under some conditions, recycled content may yield more benefits than costs. Research limitations/implications I define the phenomenon of content recycling and differentiate it from related terms. I offer propositions for future research. Practical implications I inform managers of the benefits and costs of recycling content and conditions under which benefits may override costs. Originality/value The research is novel and helps develop a common managerial practice.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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