Defining the Voice of Montreal: Exploring Possibilities for Human-Machine Companionship
Bibliographic record
Abstract
This thesis explores the possibility for (re)defining the relationship between humans and robots with conversational interfaces. It does so by looking at the creation process of a voice and text-based virtual assistant for tourists from the perspective of critical posthumanism and posthuman performativity. In a reflexive fashion, I analyze my involvement in a tech start-up, in order to argue that, rather than being ontologically separated, the boundaries between humans and machines are culturally and historically constructed. Moreover, through a closer look at the development of my own relationship with the prototype of the voicebot and my performance of ‘demos’ among uninitiated users, I put forward a relational understanding of how humans and machines become with - and constantly remake - each other. This allows me to redefine our relationship with intelligent artefacts beyond mere instrumentality, towards a form of human-machine companionship that highlights the potential of the relationship. Finally, through a practical engagement with the idea of posthuman responsibility, I analyse the effects of specific features of the voicebot and imagine how the boundaries between human and machines can be re-configured in a more responsible way. Amongst other things, this allows us to re-contextualize labor relations associated with service work and product development, with respects to how these practices shape gender and race. The thesis concludes by stating the relevance of these results for the fields of tourism and product development.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".