OER·harvester

Competency catalogue

The 104 active T2.3 competencies: the vocabulary the harvester searches with, the descriptor the alignment reads, and what the alignment found. Compose a harvest →

What the alignment foundHow often each was searched

How many resources the alignment reads as contributing to each competency or developing it: decided at level 2 or 3, or not decided yet and more likely than not to reach 2. 54 competencies have none, 24 one or two. Every figure is a lower bound: on a sample graded on every competency, the map counts 33% of what was graded 2 or 3, and almost nothing of the 5 competencies underlined with dots.

How many times a harvest has searched for each competency, once for each source it asked. 101 competencies have never been searched. A search is not material found: the aligned view says what came of it.

none 1–2 3–9 10–29 30+
never 1–2 3–5 6–11 12+
DDigital 94212203420012149086012303134 GGreen 0000000102000010000100010 RResilience & Entrepreneurial 004000000000000010000120000 TDeep Tech 72643202372825212335201140010

G23 Green · Attitude · Foundation

Digital Sobriety

Practice intentional and minimal use of digital resources

Alignment

0 resources likely to contribute to it or develop it

None found yet in what the alignment has read.

What the map misses here is not measured: the graded sample holds no pair graded 2 or 3 for it.

Retrieval

0 resources harvested by searching its terms

Never searched yet.

Search vocabulary

Domain group
digital sobrietydigital sufficiencyminimal computingintentional technology usedigital minimalismlow-tech approachresource-conscious computing

Zenodo and arXiv take the whole group in one query. GitHub takes one phrase at a time and works left to right, stopping once the competency is satisfied — so on that lane this order decides which terms actually run.

The compiled queries
zenodo ("artificial intelligence" OR "machine learning" OR "generative AI" OR "deep learning" OR "reinforcement learning" OR "large language model" OR "AI") AND ("digital sobriety" OR "digital sufficiency" OR "minimal computing" OR "intentional technology use" OR "digital minimalism" OR "low-tech approach" OR "resource-conscious computing")
arxiv (all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"digital sobriety" OR all:"digital sufficiency" OR all:"minimal computing" OR all:"intentional technology use" OR all:"digital minimalism" OR all:"low-tech approach" OR all:"resource-conscious computing")
github · term 1 AI "digital sobriety" in:name,description,readme fork:false archived:false
github · term 2 AI "digital sufficiency" in:name,description,readme fork:false archived:false
github · term 3 AI "minimal computing" in:name,description,readme fork:false archived:false
github · term 4 AI "intentional technology use" in:name,description,readme fork:false archived:false
github · term 5 AI "digital minimalism" in:name,description,readme fork:false archived:false
github · term 6 AI "low-tech approach" in:name,description,readme fork:false archived:false
github · term 7 AI "resource-conscious computing" in:name,description,readme fork:false archived:false

Profile 22a8f1b564526d3e

Alignment descriptor draft

Outcomes
  • Can decide when a digital or AI tool is not needed.
  • Can practise intentional, minimal use of digital resources.
  • Can explain digital sufficiency and its benefits.
Develops it

Material on digital sobriety — sufficiency, minimal use, low-tech alternatives — with reflection on one's own use.

Only mentions it

A passing remark about using fewer devices.

Alignment terms
digital sobrietydigital sufficiencylow-techfrugalityintentional use

Read in a resource's text beside the domain group. They are never compiled into a search.

Neighbours

Descriptor 5ea85a286654

How a vocabulary becomes a search

Every candidate must match both concept groups in its title, description or keywords before any content is downloaded. Precision warnings are recorded for audit; they never silently cancel positive evidence.

  1. Official name and description
  2. AI/ML terms and domain terms
  3. Zenodo, arXiv and GitHub syntax
  4. Metadata relevance gate
Mandatory AI/ML group
artificial intelligencemachine learninggenerative AIdeep learningreinforcement learninglarge language modelAI

GitHub's repository search takes one phrase at a time. Measured against the live endpoint, a parenthesised OR group returns nothing at all where each term alone returns thousands, and the 256-character limit rejects a long group outright — so that lane issues one query per term and stops as soon as the competency is satisfied.

coverage-vocabulary-1.3.0+sha256:08ad2cd19868 · catalogue sha256:04970bfc77255b1f

What a descriptor is

A descriptor is how the alignment reads a competency: what a learner who has it can do, what a resource has to do to develop it, what only mentions it, the phrases its subject is written in, its false friends, and how to tell it from the competencies that share its words. The definition above it stays the authority.

Every descriptor is a draft until a partner approves it; 0 of 104 are approved. Read the alignment rubric →

competency-descriptors-1.0.0+sha256:3cc741a61ced