Real multiplicities: post-identity and the changing face of arts education
Bibliographic record
Abstract
Being human is a finite entity, defined by specific qualities, ideals, or characteristics. In Deleuzian terms, the posthuman is a stage of transition, never reaching representation because it is always changing and becoming. This dissertation explores the subjectivity of mostly Canadian, contemporary artists and arts teachers as informed and negotiated by the posthuman. Six secondary and post secondary arts educators submitted artworks and artist statements for this study. Theirs’, and the artworks and responses of David Hoffos, Nancy Paterson, Catherine Richards, David Rokeby, Jana Sterbak, Nell Tenhaaf, and Norman White, through published works, past interviews, and personal websites, have been critically examined. Furthering arts-based research, I have presented some material through a graphic novel format and interpreted findings through my own artistic video response. Identity and post-identity issues have been examined through Lacanian and Deleuzian/Guattarian critical social theories, exploring their affects on arts education. An accompanying website has been created to communicate the study, provide access to required forms, enable communication and collaboration between artists and educators, and provide a final web-based exhibition of the art and results of this study. This arts-based research reveals the shifting desires of participant artists, teachers, and researcher, as desiring machines. It suggests that post-identity structures are located in the psychic Real, which are largely and unethically untapped in current humanist education. While both old and new media are currently used in art making, new forms of visual media advance understandings of identity by revealing how artist and teacher identities are changing with technology, and with the posthuman visual culture in which they negotiate. Art education and visual studies have a vested interest in this visual culture. This is applied to a transformative visual studies curriculum at the teacher education level, where suggested practices will result in a direct impact on curriculum and pedagogy within the general school system as new teachers enter the field. This can bring art education to a place that better fits the heavily visual, cyber savvy society in which our current learning community lives and creates.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".