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
<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; tab-stops: .5in 1.0in 1.5in 2.0in 2.5in 3.0in 3.5in 4.0in 4.5in 5.0in 5.5in right 6.0in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Co-branding is an increasingly popular technique used primarily in domestic markets to transfer the positive associations of the partner brands to a newly formed co-brand.<span style="mso-spacerun: yes;">&nbsp; </span>This exploratory study investigates the relative impact of the brand equity of the constituent brands on co-branding efforts internationally using a sample of 1,203 Philippine housewives.<span style="mso-spacerun: yes;">&nbsp; </span>Findings indicate the co-branding of two high-equity brands was mutually beneficial, but the co-branding of high-equity and low-equity brands can be potentially dangerous for the high equity partner.</span></span></p>
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 | 0.007 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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