PARTICIPATIVE MULTIPLE CRITERIA APPROACH FOR DIGITAL INFLUENCERS CHOICE
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".