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

G03 Green · Skill · Intermediate

Sustainable AI Model Selection

Choose AI models that minimize environmental impact

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
sustainable model selectionenergy-efficient modelmodel selectionsmall language modelmodel efficiencyenvironmental model comparisonefficient AI model

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 ("sustainable model selection" OR "energy-efficient model" OR "model selection" OR "small language model" OR "model efficiency" OR "environmental model comparison" OR "efficient AI model")
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:"sustainable model selection" OR all:"energy-efficient model" OR all:"model selection" OR all:"small language model" OR all:"model efficiency" OR all:"environmental model comparison" OR all:"efficient AI model")
github · term 1 AI "sustainable model selection" in:name,description,readme fork:false archived:false
github · term 2 AI "energy-efficient model" in:name,description,readme fork:false archived:false
github · term 3 AI "model selection" in:name,description,readme fork:false archived:false
github · term 4 AI "small language model" in:name,description,readme fork:false archived:false
github · term 5 AI "model efficiency" in:name,description,readme fork:false archived:false
github · term 6 AI "environmental model comparison" in:name,description,readme fork:false archived:false
github · term 7 AI "efficient AI model" in:name,description,readme fork:false archived:false

Profile 2091839a611051ea

Alignment descriptor draft

Outcomes
  • Can compare candidate models on their energy and resource cost as well as their accuracy.
  • Can choose a smaller or more efficient model when it meets the need.
  • Can justify a model choice on environmental grounds.
Develops it

Material that teaches choosing models with their environmental cost in view — efficiency benchmarks, small models, trade-offs — with cases where the learner makes the choice.

Only mentions it

Choosing among models for accuracy alone, or sklearn's model_selection module in code.

Alignment terms
small language modelsefficient modelsenergy-efficient modelmodel sizecarbon-aware model selection

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

False friends
sklearn model selectionmodel selection importtrain test splitcross validation

A term found beside one of these is read as another sense of the words, not as this competency.

Neighbours

Descriptor e0eddeda14ed

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