Designing Educational Experiences: Resurrecting the Archive through Collaborative Exhibition
Bibliographic record
Abstract
This article considers the creation of an exhibition at the National Library of New Zealand. Specifically, this project was a collaborative endeavour between the School of Design at Victoria University of Wellington and the National Library, aimed at utilising new technologies and traditional archival research to bring forgotten and rarely accessed data back to life in a public exhibition. From the outset this project sought to combine the best of both new educational technologies (augmented reality) with tactile, physical materials (created using laser cutting and 3D printing) that the public could handle and use in a way that would break down the barriers between exhibition objects and resurrected archival data. The result was an interactive exhibition focused upon the emergence of Wellington as a city and the growth of its waterfront over the last century and a half. The design research project took place between November 2016 and February 2017 and resulted in a project exhibition open to the public running from May 2017 to February 2018. The principle supervisors were Leon Gurevitch and Tim Miller with a research team of three research assistants (Stefan Peacock, Alasdair Tarry and Louis ElwoodLeach).
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".