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
The accelerated growth of sponsorship has brought increased attention and scrutiny to this relatively new area of marketing and communications strategy. In turn, researchers have focused on defining, understanding and measuring the various aspects of sponsorship. However, detailed research related to the ‘how’ of sponsorship implementation remains limited. A key aspect of implementation is known as activation, which refers to the investment by the sponsor above and beyond the fee required to acquire the official rights to that sponsorship. Activation is normally referred to as a ratio of the additional investment to the cost of the rights fees. Previous studies have offered recommended activation ratios ranging from 1:1 to as high as 8:1 in order to fully reap the rewards of sponsorship. This research seeks to enhance our understanding of sponsorship activation via an in-depth case study, a typical method for exploratory research of this nature. Specifically, we ask (i) what drives activation, (ii) what are the best methods of activation, and (iii) how much should be spent on activation? Findings suggest that management decisions regarding activation focus on the custom development of quality strategies versus increasing the activation ratio. Indeed, a formula based on a variety of factors is recommended since activation tactics and their appropriateness to a specific sponsorship are the cornerstones of sponsorship success. Overall, results present a four-step model including activation drivers, strategic considerations, activation spending, and sponsorship outcomes.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it