Bibliographic record
Abstract
This paper analyses Hello Barbie as a commercial artefact to explore how big data practices are reshaping the enterprise of marketing. The doll uses voice recognition software to ‘listen’ to the child and ‘talk back’ by algorithmically selecting a response from 8000 predetermined lines of dialogue. As such, it is a useful example of how marketers use customer relationship management systems that rely on sophisticated data collection and analysis techniques to create a relationship between companies and customers in which both parties are positioned as active participants who are able to obtain what they wish from the interaction. I use dialogic analysis to see how Mattel ‘makes sense’ of the dialogue as a dialogic partner. I argue that, in spite of the rhetoric of instantaneity and personalization, in which the technology is positioned as an immediate response to a child’s imagination, Mattel’s dialogic communication is both asynchronous and carefully crafted to fit the child’s responses within predetermined consumer subjectivities that are crafted to encourage particular kinds of consumption. Although the dialogue spoken by Hello Barbie is able to situate Barbie as an active subject, the control exercised by the company in order to elicit data for customer relationship management purposes and steer the dialogue to brand-friendly messages relegates the child to a passive role. Accordingly, the doll fails to deliver the promises of customer relationship management.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".