Trickster Fiddles with Informatics: The Social Impact of Technological Marketing Schemes
Bibliographic record
Abstract
"Information is power if and only if you have the knowledge to know what it means, the will to use it, the ability to apply it, and access to a channel of communication" [1]. We see this in current fields of research as varied as Marketing, Philosophy, and Communications Studies, and in current issues about who owns and controls technology. But a character from a far older tradition helps explain many problems in society today with technology: Trickster, the mythical character who confuses fact with fiction, makes good use of Technoism, a term coined by Davis [2] in 1999 to denote suppressed skepticism and blind compliance with the chaotic and uncontrolled progression of technology in our lives that leads to a dangerous split between the "haves" and "have-nots" of the technology world. This paper will discuss the use of Technoism to give the public and users of technology a false sense of power and control over their lives when in fact they are being duped into a financially motivated campaign of consumer exploitation. The paper makes some recommendations for establishing a conscience in the use of technology.
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 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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".