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
Abstract Consumers’ perceptions of technology are less matters of product attributes and concrete statistical evidence and more of captivating stories and myths. Managers of IoT can instill consumer trust when they tell highly emotional stories about the technologically empowered self, home, family or society. The key benefit of this approach is that storytelling-based IoT marketing allows consumers to forge strong and enduring emotional bonds with IoT and, in many cases, to develop loyalty beyond belief. However, stories aren’t always positive. Negative stories and meanings about a technology that are circulated in popular culture can be dangerous and harmful to a brand or a new technology. Regardless of its source, marketers need to understand the nature of the doppelgänger images that may be circulating for their technologies. They can be regarded as diagnostic tools to better understand how consumers think about and experience their IoT solutions. Also, doppelgänger narratives are valuable raw ingredients from which marketers can cull new, more captivating IoT stories that nurture consumer adoption.
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 | 0.006 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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