Thick Data: A New Qualitative Analytics for Identifying Customer Insights
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
All sort of businesses and organizations are now online, and they leave a trail of data on social media sites, blogs and portals, messages of all types, and lots of traces on search engines. Enterprises can no longer escape the need to monitor and analyze social media outlets such as Facebook, Twitter, Pinterest, news sites, blogs, forums, video sites, and microblogs. To succeed and grow, a business needs to be able to acquire, retain, satisfy, and engage their customers effectively. Embracing social media analytics is vital for assessing how well the business does this. Social media analytics is the process of accessing data generated on social media such as ideas, sentiments, and customer feedback. This information can then be analyzed and fed into the decision making process across all business activity, including campaign orchestration, product development, recruitment, customer advocacy and engagement processes, sales input, and much more.
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 | 0.000 | 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 | 0.000 |
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