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

G25 Green · Skill · Intermediate

Green AI Innovation Practice

Propose lower-energy or lower-data alternatives when designing or selecting AI solutions

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
green AI innovationlow-energy alternativelow-data AIfrugal AIresource-efficient innovationsustainable solution designminimal data approach

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 ("green AI innovation" OR "low-energy alternative" OR "low-data AI" OR "frugal AI" OR "resource-efficient innovation" OR "sustainable solution design" OR "minimal data approach")
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:"green AI innovation" OR all:"low-energy alternative" OR all:"low-data AI" OR all:"frugal AI" OR all:"resource-efficient innovation" OR all:"sustainable solution design" OR all:"minimal data approach")
github · term 1 AI "green AI innovation" in:name,description,readme fork:false archived:false
github · term 2 AI "low-energy alternative" in:name,description,readme fork:false archived:false
github · term 3 AI "low-data AI" in:name,description,readme fork:false archived:false
github · term 4 AI "frugal AI" in:name,description,readme fork:false archived:false
github · term 5 AI "resource-efficient innovation" in:name,description,readme fork:false archived:false
github · term 6 AI "sustainable solution design" in:name,description,readme fork:false archived:false
github · term 7 AI "minimal data approach" in:name,description,readme fork:false archived:false

Profile dcb7ca0efd69e1be

Alignment descriptor draft

Outcomes
  • Can propose a lower-energy or lower-data alternative when designing an AI solution.
  • Can judge whether a simpler method meets the need.
  • Can argue for frugal design to stakeholders.
Develops it

Material on frugal AI design — simpler models, less data, non-AI alternatives — with design problems the learner works through.

Only mentions it

A note that a smaller model was faster.

Alignment terms
frugal ailightweight modelless datasimpler alternativeresource-efficient design

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

Neighbours

Descriptor 82afbe01f5da

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