Why We Should Encourage Diverse Taxonomies—Not Force a Master Skills Language

In the fast-moving world of education, micro-credentialing, and Learning and Employment Records (LERs), one of the most fascinating challenges we face is this:
How do we translate deeply human, culturally specific ways of describing work into structured data taxonomies?

Every organization has its own language.
Every field develops its own dialect.
Every generation invents new vocabulary to describe familiar ideas.

The way a Gen Z learner talks about collaboration differs from how a Boomer manager might describe the same skill. A hospital, a ranch, a film studio, and a software startup all use entirely different words to express what “good work” looks like.

In other words: taxonomy diversity isn’t a problem—it’s inevitable.
And it’s valuable.

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Organizations Speak in Dialects, Not Dictionaries

Just as families, regions, and cultures naturally develop their own dialects, professional communities develop specialized vocabularies to express what matters to them.

• A welding instructor talks about puddle control.
• A UX designer talks about user flow empathy.
• A nurse talks about patient acuity prioritization.
• A nonprofit leader talks about community activation.
• A sales team talks about pipeline hygiene.

These terms carry meaning within context. They are efficient, precise, and expressive. They allow people to describe work in their own voice.

A single “master skills taxonomy” can’t contain all of this richness—nor should it try.

So instead of forcing convergence, the micro-credentialing and LER ecosystem should be doing something different:
encouraging organizations, communities, and individuals to build their own taxonomies intentionally and proudly.

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But Taxonomies Cannot Exist in Isolation

Here’s the key nuance:
Diverse taxonomies are powerful, but only if they can be interpreted, translated, and connected.

Without reference points, each taxonomy becomes an island.

This is why the LER and micro-credentialing ecosystem relies on public registries, shared frameworks, and industry taxonomies to serve as translators—a kind of modern Rosetta Stone for skills.

Reference taxonomies don’t replace organizational dialects;
they anchor them.

By allowing a skill tag, badge, or competency to “point to” a known framework:

• Humans understand what the creator intended
• Machines can map relationships more intelligently
• Employers gain clarity about what a credential actually means
• Learners can articulate their abilities across contexts

This “anchoring” is what makes the ecosystem interoperable.

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Why Reference Points Matter: The Problem of Ambiguous Skills

Many skill terms are inherently ambiguous.

Take the word “management.”
It could refer to:

• A job role
• A competency
• A leadership philosophy
• A technical skill (e.g., “risk management”)
• A soft skill (e.g., “time management”)

Without context, the term is meaningless.

But if a credential explicitly references a known taxonomy—such as O*NET’s role taxonomy or ESCO’s skill framework—the intended meaning becomes instantly clear.

Reference points remove ambiguity and enhance precision, which is essential in micro-credentialing, hiring, academic design, and any system built on skill signals.

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Credential Description Standards Bring Order to Complexity

Frameworks like the Credential Transparency Description Language (CTDL) help establish clarity by enabling credential issuers to:

• Describe what a credential represents
• Specify which taxonomy or registry it references
• Provide machine-readable metadata
• Improve searchability, comparability, and trust
• Communicate meaning across institutions

In other words, CTDL isn’t about forcing uniformity—it’s about enabling translation.

A diverse ecosystem still needs a shared syntax.

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Emerging Standards Are Making Skills More Contextual and Visual

The micro-credentialing field continues to evolve, and several promising standards are pushing the ecosystem toward richer, more meaningful skill data.

Open Badges 3.0

Allows credentials to contain deeper, more structured metadata—making the “story behind the badge” more useful, transferable, and insightful.

STAMP (Standardized Talent Asset Mapping Protocol)

Supports quantifying and modeling the relationships, scale, and context of skill tags within real experiences.
This allows credentialing systems to show how a person applied skills—not just list which ones were present.

These advancements move us from flat, checklist-style credentials to multi-dimensional skill representations, enabling learners to visualize capability, not merely claim it.

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So Yes: Build Your Own Taxonomies—Just Don’t Build Them Alone

The diversity of taxonomies in the micro-credentialing and LER ecosystem is not a flaw; it’s evidence of the richness and complexity of human work.
We shouldn’t suppress it—we should celebrate it.

But for those taxonomies to be meaningful to others, they must be:

• Anchored to shared reference frameworks
• Described transparently
• Mapped in ways machines and humans can interpret
• Connected to ecosystem standards like CTDL, Open Badges 3, and STAMP

This balance—local creativity plus global interoperability—is what will unlock the future of true skills mobility.

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The Future: A Translatable, Connected Landscape of Human Capability

Micro-credentials will only reach their full potential when they are:

• Expressed in the authentic language of their creators
• Connected through shared reference points
• Interpretable by employers and educators
• Portable across systems
• Rich in context, evidence, and meaning

This is the future we should be working toward:
a world where no taxonomy has to give up its identity to be understood—and no learner has to reshape their story to fit a rigid system.

A world where skills are not only verified, but contextualized.
Not only tagged, but translated.
Not only recorded, but truly understood.