Bibliographic record
Abstract
In recent years gamification has emerged as a design trend in customer relationship management, marketing, education and governance. It promotes the use of game design principles in the organization of every day environments, tasks and interactions. As an offspring of advanced communication technologies, gamification relies on the unhindered use of networked devices that transforms every experience into a user experience. Borrowing on the ubiquitous popularity of video games, the premise of gamification is the technologically enabled relationship between virtual causes and real-life effects, and its promise - a mutually beneficial coordination of corporate and personal interest. This dissertation outlines the socio-political implications of the concept of gamification through a critical examination of its content and intended meanings. The unpacking of gamification as an aspiration and a worldview reveals that as soon as we take for granted the equality of the sign and the signified, we also accept that life experiences do not exceed the signs we use to describe them. Therefore, to play life as a game, as gamifiers urge, is to live life by design. The definition I coin considers gamification from the perspective of political consequences, rather than practical application and mechanics. I work towards this definition by focusing on the rhetoric of gamification as an expressed intention that constructs motives and renegotiates beliefs. Hence, the theoretical model I apply draws on the work of two major theorists. American rhetorician and philosopher Kenneth Burke offers a theoretical apparatus for the study of the form and rhetorical devices of addressed messages. French semiotician and social theorist, Jean Baudrillard, informs the deconstruction of the claims gamification makes. The treatment of language as intention and action that is necessarily subjective and interested, offers a liminal stand-point from where the vision of a gamified world can be seen as an ideology which normalises itself by rhetorical means. Thus, I propose that the concept of gamification, whether applied in practice or not, is a political act. It constructs an ideology that seeks to reconcile the myth of the sacrosanct freedom of the Western individual with the constant imposition of corporate and government demands for compliance, accountability and efficiency.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".