PARTICIPATIVE MULTIPLE CRITERIA APPROACH FOR DIGITAL INFLUENCERS CHOICE
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
Social networks play an essential role in consumers' decision-making. In this sense, digital influencers are tools for better reaching the marketing objectives of an organization. However, choosing the digital influencer that better represents the company's image is a challenge for marketing departments. This type of decision is currently made intuitively and unstructured. This research proposes a participatory approach to support the selection of digital influencers in marketing planning. The methodology is structured in five phases: (i) setting out a list of hypothetical potential digital influencers, (ii) defining the criteria to assess the potential digital influencers, (iii) assessing the performance of the potential digital influencers in each criterion, (iv) aggregating the results to obtain the ideal portfolio and (v) analysis of the method results and influencer choice. The approach was tested and validated in a tourism company in Brazil. As a result, the potential digital influencers named were chosen in the proposed method.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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