Interview scorecard template

Digital Humanities Researcher interview scorecard

Evaluate Digital Humanities Researcher candidates across 4 weighted areas: theoretical command, from theory to hardware or code, research judgement, and explaining it to non-specialists. Theoretical command leads at 35%, so check command of humanities method plus computational theory: TEI P5 encoding decisions, topic modelling assumptions, distant reading critiques, FAIR and Linked Open. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For theoretical command, look for evidence the candidate names specific encoding or modelling choices, cites the scholarly debate behind them, and states where quantitative method breaks down interpretively. For from theory to hardware or code, look for evidence the candidate points to live URLs, repositories with commit history, and documents their own role in code, encoding, or data curation. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Theoretical command

35% weight

Check command of humanities method plus computational theory: TEI P5 encoding decisions, topic modelling assumptions, distant reading critiques, FAIR and Linked Open Data vocabularies such as CIDOC-CRM.

Evidence to listen for

  • Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
  • Knows which results are established and which are contested
  • Distinguishes their own contribution from the field's
  • Comfortable saying where the theory runs out

Five-point scoring guide

1
Poor

Recites terminology without understanding; cannot go one layer deeper.

2
Needs Improvement

Surface familiarity; conflates established results with speculation.

3
Satisfactory

Solid grasp of the core theory; thin at the frontier.

4
Very Good

Strong command; separates settled results from open questions.

5
Excellent

Names specific encoding or modelling choices, cites the scholarly debate behind them, and states where quantitative method breaks down interpretively.

02
Evaluation factor

From theory to hardware or code

30% weight

Ask what they built and shipped: digital editions in Omeka or Scalar, IIIF manifests, Python or R pipelines, OCR/HTR workflows in Transkribus, cleaned datasets on GitHub or Zenodo.

Evidence to listen for

  • Has built, simulated, or run something real, not only published about it
  • Knows the gap between the idealised model and the actual apparatus or system
  • Names the practical constraint that dominates in real conditions
  • Can describe a result that did not match prediction

Five-point scoring guide

1
Poor

Purely theoretical; no contact with implementation.

2
Needs Improvement

Some exposure but unaware of practical constraints.

3
Satisfactory

Has implemented work; understands the main real-world limits.

4
Very Good

Strong practical record; articulate about theory-versus-reality gaps.

5
Excellent

Points to live URLs, repositories with commit history, and documents their own role in code, encoding, or data curation.

03
Evaluation factor

Research judgement

20% weight

Probe how they choose corpora and scope: handling OCR noise, sampling bias in archives, copyright limits, deciding when a research question suits computation at all.

Evidence to listen for

  • Chooses problems by tractability and value, not novelty alone
  • Knows when to abandon a line of work
  • Reads and evaluates others' results critically
  • Can say what would falsify their own approach

Five-point scoring guide

1
Poor

Chases novelty; no sense of tractability or when to stop.

2
Needs Improvement

Weak problem selection; persists past the point of value.

3
Satisfactory

Reasonable judgement within a defined programme.

4
Very Good

Selects problems well and knows when to abandon a line.

5
Excellent

Describes abandoning or reframing a method after evidence, and explains sampling gaps in the archive rather than glossing over them.

04
Evaluation factor

Explaining it to non-specialists

15% weight

Look for translation work: grant narratives for NEH or AHRC, briefings for librarians and archivists, teaching workshops, public exhibits built from their data.

Evidence to listen for

  • Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
  • Writes clearly
  • Collaborates across disciplines
  • Makes the case for resources in terms the audience cares about

Five-point scoring guide

1
Poor

Cannot communicate outside their specialism.

2
Needs Improvement

Explanation is either impenetrable or hollow.

3
Satisfactory

Adequate with technical peers; less effective with lay audiences.

4
Very Good

Explains clearly to specialists and non-specialists alike.

5
Excellent

Explains a network graph or model output in plain language to curators and funders, with concrete examples of funded or adopted proposals.

Evidence-led prompts

Interview questions for a Digital Humanities Researcher

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe a project where you applied computational methods to humanities research?

  2. 02

    Have you published research that combines digital methods with humanities work?

  3. 03

    Have you worked on collaborative projects, and what was your role?

  4. 04

    What is your experience working with digital archives and collections?

  5. 05

    Which programming languages have you used, and how in your research?

See the complete Digital Humanities Researcher question set
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