Welcome! Spooky Virtual Agents: Enter the World of Ghosts
Bibliographic record
Abstract
Abstract. As an alternative to naturalistic and servile virtual agents highlighted in recent years this paper presents idiosyncratic, supernatural and attentionbegging assistants as a new approach to agent based assistance. The paper portrays a society of disembodied, location-specific conversational agents (DELCA) called ‘Ghosts ’ inhabiting a social Darwinist interactive narrative universe designed to promote both engaging and quality services. The paper presents the conceptual framework behind the ongoing trans-disciplinary work with DELCA Ghosts through scenarios and profiles. In addition we point to future research and perspectives related to entertaining ambient intelligence in general and DELCA Ghosts in specific. Imagine entering the beautiful new building hosting The IT University in Copenhagen (ITU). In the hall a somewhat flickering and vague silhouette appearing on the window by the front desk welcomes you with a slightly rusty voice but a distinguished British dialect:- “Anne de Waal, I presume”. It’s the butler who was notified of your appointment by the ITU schedule calendar and detected your WLAN enabled PDA as you entered the
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".