Bibliographic record
Abstract
In the current time of information, we live in times that the expectations of thinkers as Lévy (1998) have become a reality. The profile of the consumer has changed as he connected with other consumers and begun to demand that there is a dialogue between the company and society. This new scenario required that technology was further developed and that the process of communication was made faster by means of online platforms that make the exchange of information viable and encourages it. The crowdsourcing arose with the availability of the crowd’s intellectual capital that has changed the way companies interact and organize themselves as they come to realize the strategic importance of this information. In order to fully understand this new scenario, we have created three hypothesis that have served as guide for this paper, which are: H1 – Crowdsourcing affects products’ performance; H2 – negative reviews from crowd voting provide the same effects that positive reviews; and H3- Crowd voting provides information about consumer`s perception of the product being offered. Therefore, this paper aims to understand if the crowdsourcing affects product’s performance of the companies that use crowdsourcing by analyzing the theoretical basis of crowdsourcing and also by reviewing a data-base with reviews made by consumers during a four-year period. Based on the review of the correlations between the variables obtained in Amazon’s data base based on the reviews offered by the consumers, this work suggests that the crowdsourcing does affect the product’s performance.
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.
How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".