Land Grab CT: Leveraging Branding Principles as Design and Publishing Tools
Bibliographic record
Abstract
The project "Land Grab CT" is ongoing at the University of Connecticut, and is a collaboration between members of Greenhouse Studios, the Native American and Indigenous Students' Association, and the UConn Human Rights Institute. A major focal point of the project was a website that contextualizes the University of Connecticut as a colonial institution within its status as a land grant institution funded by the Morrill Act of 1862. Other activities included speaker series, dialogue events, interviews, and a social media campaign that were all directed towards educating the University of Connecticut community about its colonial history and current disparity in resources and relationships with the Native community. Because of the wide range of dissemination pathways and need for community engagement, the project team incorporated branding design from the beginning of the project lifecycle to help find a cohesive project voice and to tailor content and design towards the expected audience. This paper illustrates the design process of the project, the integration of branding principles, and highlights some real and potential difficulties in using branding principles in public digital humanities projects.Le projet "Land Grab CT" en cours à l'Université du Connecticut est le résultat d'une collaboration entre les membres de Greenhouse Studios, l'Association des étudiants indigènes et amérindiens (NAISA) et l'Institut des droits de l'homme de l'Université du Connecticut. L'un des principaux axes du projet est la création d'un site web qui situe l'université du Connecticut en tant qu'institution coloniale dans le cadre de son statut d'institution d'octroi de terres financée par la loi Morrill de 1862. D'autres activités ont été organisées, notamment des séries de conférences, des dialogues, des entretiens et une campagne dans les reseaux sociaux, tous destinés à informer la communauté de l'université du Connecticut sur son histoire coloniale et sur les disparités actuelles en matière de ressources et de relations avec la communauté autochtone. En raison du large éventail de voies de diffusion et de la nécessité d'un engagement communautaire, l'équipe du projet a intégré la stratégie de marque depuis le début du cycle de vie du projet afin de trouver une voix cohérente et d'adapter le contenu et la conception à l'audience attendue. Cet article vise à presenter le processus de conception du projet, l'intégration des principes de stratégie de la marque et met en évidence certaines difficultés réelles et potentielles liées à l'utilisation des ses principes dans les humanités numériques publiques.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".