Leveraging corporate philanthropy and community partnerships to maximize earned media
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
Relationships between corporations and community-based not-for-profit organizations have evolved over the years from just donations and sponsorships, to integrated partnerships and collaborations known as strategic philanthropy. When details of these philanthropic relationships and activities are covered by traditional media or shared organically on social media, the coverage is known as earned media. Earned media has the potential to influence stakeholders’ awareness, perceptions, and actions toward an organization because an organization’s publics regard this coverage as highly credible and reliable. Effectively leveraging earned media provides an important benefit to both for-profit and not-for-profit organizations. This article explores the literature, case studies, and interviews to understand earned media in terms of the philanthropic relationships and partnerships behind it, the methods and approaches that generate it, and its impact on corporations and not-for-profits. ©Journal of Professional Communication, all rights reserved.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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